Michael Garratt, Malgorzata Lagisz, Johanna Stärk, Christine Neyt, Michael Stout, José V. V. Isola, Veronica Cowl, Nannette Driver-Ruiz, Ashley D. Franklin, Monica M. McDonald, David Powell, Susan L. Walker, Jean-Michel Gaillard, Dalia A. Conde, Jean-François Lemaître, Fernando Colchero and Shinichi Nakagawa
#dat_full <- read_csv(here("data", "dat_07072021.csv"), na = c("", "NA")) #dat_full <- read_csv(here("data", "data_15022022.csv"), na = c("", "NA"))dat_full <-read_csv(here("data", "literature", "data_05052022.csv"), na =c("", "NA"))#glimpse(dat_full)#loading data ####dat_full %>%filter(is.na(Treatment_lifespan_variable) ==FALSE) %>%filter(Type_of_sterilization !="Vasectomy") %>%mutate_if(is.character, as.factor) -> dat#dim(dat)#dim(dat_full)# separating two kindseffect_type <-ifelse(str_detect(dat$Lifespan_parameter, "Me"), "longevity", "mortality")# splitting longevity into two types where they have SD or noteffect_type2 <-ifelse(str_detect(dat$Lifespan_parameter, "Me") &is.na(dat$Error_control_SD), "missing", effect_type)#fix a typo in species namedat$Species_Latin <-gsub("Macaca Fascicularis", "Macaca fascicularis", dat$Species_Latin) dat$Species_Latin <-gsub("Equus caballus", "Equus ferus", dat$Species_Latin)# creating the phylo columndat$Phylogeny <-sub(" ", "_", dat$Species_Latin)dat$Effect_type <- effect_typedat$Effect_ID <-1:nrow(dat)# key variables#names(dat)#unique(dat$Species_Latin)kable(dat, "html") %>%kable_styling("striped", position ="left") %>%scroll_box(width ="100%", height ="250px")
Order_extracted
Study
Controlled_treatments
Type_of_sterilization
Gonads_removed
Control_treatment
Shamtreatment_moderator
Sex
Species_Latin
Species
Strain
Environment
Wild_or_semi_wild
Age_at_treatment
Maturity_at_treatment
Maturity_at_treatment_ordinal
Duration_of_treatment
Shared_control
Control_lifespan_variable
Treatment_lifespan_variable
Opposite_sex_lifespan_variable
Error_control
Error_experimental
Error_opposite_sex
Lifespan_parameter
Lifespan_unit
Error_unit
Error_control_SD
Error_experimental_SD
Error_opposite_sex_SD
Coefficent_difference_to_control
Lower_interval
Upper_interval
Coefficent_unit
Sample_size_control
Sample_size_sterilization
Sample_size_opposite_sex
Notes
Notes2
Notes3
Phylogeny
Effect_type
Effect_ID
1
Kirkpatrick and Turner 2004
No
porcine zona pellucida (PZP) immunocontraception
No
untreated
No
Female
Equus ferus
Horses
NA
Wild
Yes
2 Years
Adult
4
Less than 3 years
1
6.4700
10.2700
10.300
0.850
0.5600
0.840
Mean
years
S.E.M
5.508630
1.857310
6.285984
NA
NA
NA
NA
42
11
56
Sterilization requires booster injections and these did not receive, the longer treatment did
NA
NA
Equus_ferus
longevity
1
2
Kirkpatrick and Turner 2004
No
porcine zona pellucida (PZP) immunocontraception
No
untreated
No
Female
Equus ferus
Horses
NA
Wild
Yes
2 Years
Adult
4
More than 3 years
1
6.4700
19.9400
10.300
0.850
1.6600
0.840
Mean
years
S.E.M
5.508630
7.235772
6.285984
NA
NA
NA
NA
42
19
56
NA
NA
NA
Equus_ferus
longevity
2
3
Jacob et al 2004 A
Yes
Tubul-ligation
No
Intact (no surgery)
No
Female
Rattus argentiventer
Ricefield rats
NA
Outdoor enclosure
No
Unknown - wild caught (approx 100g)
Adult (young)
4
NA
2
0.2800
0.6700
NA
6.000
34.0000
NA
Survival rate (%)
One breeding season
S.E.M
NA
NA
NA
NA
NA
NA
NA
18
6
NA
25% population sterilized data from two enclosures pooled
The impact of sterilized females on enclosed populations of ricefield rats
Estimated age at treatment from data on weight at surgery. They were approximately 100g and are never referred to as sexually immature. 1Pregnancy occurs from when the animals are 60-120 g in weight (Sudarmaji 2002).
Rattus_argentiventer
mortality
3
4
Jacob et al 2004 A
Yes
Tubul-ligation
No
Intact (no surgery)
No
Female
Rattus argentiventer
Ricefield rats
NA
Outdoor enclosure
No
Unknown - wild caught (approx 100g)
Adult (young)
4
NA
3
0.1400
0.2500
NA
6.000
8.0000
NA
Survival rate (%)
One breeding season
S.E.M
NA
NA
NA
NA
NA
NA
NA
12
12
NA
50% population sterilzed data from two enclosures pooled
The impact of sterilized females on enclosed populations of ricefield rats
NA
Rattus_argentiventer
mortality
4
5
Jacob et al 2004 A
Yes
Tubul-ligation
No
Intact (no surgery)
No
Female
Rattus argentiventer
Ricefield rats
NA
Outdoor enclosure
No
Unknown - wild caught (approx 100g)
Adult (young)
4
NA
4
0.2200
0.1700
NA
6.000
6.0000
NA
Survival rate (%)
One breeding season
S.E.M
NA
NA
NA
NA
NA
NA
NA
6
18
NA
75% population sterilized data from two enclosures pooled
The impact of sterilized females on enclosed populations of ricefield rats
NA
Rattus_argentiventer
mortality
5
6
Jacob et al 2004 B
Yes
Tubul-ligation
No
Intact (no surgery)
No
Female
Rattus argentiventer
Ricefield rats
NA
Wild
Yes
Unknown - wild caught (approx 100g)
Adult (young)
4
NA
10
0.4100
0.4200
NA
0.110
0.1700
NA
Survival rate (%)
two months
deviance
NA
NA
NA
NA
NA
NA
NA
13
24
NA
Radiocollared - Animals spread across 4 plots giving error for survival
NA
NA
Rattus_argentiventer
mortality
6
7
Jacob et al 2004 B
Yes
Progesterone treatment
No
Untreated
No
Female
Rattus argentiventer
Ricefield rats
NA
Wild
Yes
Unknown - wild caught (approx 100g)
Adult (young)
4
NA
10
0.4100
0.4000
NA
0.110
0.0000
NA
Survival rate (%)
two months
deviance
NA
NA
NA
NA
NA
NA
NA
15
24
NA
Radiocollared -Animals spread across 4 plots giving error for survival. Progesterone treatment wore off and some females got pregnant
NA
NA
Rattus_argentiventer
mortality
7
8
Twigg et al 2000
Yes
Tubul-ligation
No
Sham surgery or no surgery
No
Female
Oryctolagus cuniculus
Rabbit
NA
Outdoor enclosure
Yes
Unknown - wild caught (see notes for age estimation)
Adult
4
NA
5
0.1330
0.4180
0.165
NA
NA
NA
Survival rate (%)
Four years
NA
NA
NA
NA
NA
NA
NA
NA
225
165
435
1993 cohorts. Assuming adults at sterilization because they do not refer to kittens, and because they show the survival of sterile females against intact females and intact adult males. They also show a plot of kitten survival and show that it is very poor
NA
NA
Oryctolagus_cuniculus
mortality
8
9
Twigg et al 2000
Yes
Tubul-ligation
No
Sham surgery or no surgery
No
Female
Oryctolagus cuniculus
Rabbit
NA
Outdoor enclosure
Yes
Unknown - wild caught - yearling. Puberty or adult?
NA
NA
NA
6
0.2390
0.3630
0.221
NA
NA
NA
Survival rate (%)
Four years
NA
NA
NA
NA
NA
NA
NA
NA
109
63
267
1994 cohorts
NA
NA
Oryctolagus_cuniculus
mortality
9
10
Twigg et al 2000
Yes
Tubul-ligation
No
Sham surgery or no surgery
No
Female
Oryctolagus cuniculus
Rabbit
NA
Outdoor enclosure
Yes
Unknown - wild caught - yearly. Puberty or adult?
NA
NA
NA
7
0.2100
0.3140
0.209
NA
NA
NA
Survival rate (%)
Four years
NA
NA
NA
NA
NA
NA
NA
NA
252
155
382
1995 cohorts - Also survival data and data split into different densities
NA
NA
Oryctolagus_cuniculus
mortality
10
11
Gipps and Jewel 1979
Yes
Castration
Yes
Sham surgery
Yes
Male
Myodes glareolus
Bank vole
NA
Outdoor enclosure
No
Immature
Prepuberty
2
NA
8
0.7820
0.9570
NA
NA
NA
NA
Survival rate (%)
6 months
NA
NA
NA
NA
NA
NA
NA
NA
23
23
NA
No reproduction in enclosure so density both treatments exposed to would be the same
NA
NA
Myodes_glareolus
mortality
11
12
Gipps and Jewel 1979
Yes
Castration
Yes
Sham surgery
Yes
Male
Myodes glareolus
Bank vole
NA
Outdoor enclosure
No
Immature
Prepuberty
2
NA
9
0.7590
1.0000
0.700
NA
NA
NA
Survival rate (%)
11 months
NA
NA
NA
NA
NA
NA
NA
NA
29
18
40
Some intacts were in a control enclosure without castrates. In the enclosure with castrates the density in the enclosure increased slightly quicker
NA
NA
Myodes_glareolus
mortality
12
13
Zakeri et al 2019
Yes
Ovariectomy
Yes
Intact (no surgery)
No
Female
Mus musculus
Mouse
NMRI
Laboratory
No
10 months
Adult (old)
4
NA
11
0.3600
0.5000
NA
NA
NA
NA
Survival rate (%)
11.5 months
NA
NA
NA
NA
NA
NA
NA
NA
16
16
NA
Its the sterilization treatment that is compared to two different types of control in this study
NA
NA
Mus_musculus
mortality
13
14
Zakeri et al 2019
Yes
Ovariectomy
Yes
Sham surgery
Yes
Female
Mus musculus
Mouse
NMRI
Laboratory
No
10 months
Adult (old)
4
NA
11
0.3300
0.5000
NA
NA
NA
NA
Survival rate (%)
11.5 months
NA
NA
NA
NA
NA
NA
NA
NA
16
16
NA
Its the sterilization treatment that is compared to two different types of control in this study
NA
NA
Mus_musculus
mortality
14
15
Dorner 1973
Yes
Castration
Yes
Untreated
No
Male
Rattus norvegicus
Rat
Sprague-Dawley-Stammes
Laboratory
No
Day after birth
Birth
1
NA
12
570.0000
696.0000
NA
122.000
132.0000
NA
Mean
days
Standard Deviation
122.000000
132.000000
NA
NA
NA
NA
NA
12
8
NA
NA
NA
NA
Rattus_norvegicus
longevity
15
16
Asdell et al 1967
Yes
Ovariectomy
Yes
Intact (no surgery)
No
Female
Rattus norvegicus
Rat
Cornell Nutrion colony
Laboratory
No
Between 38-42 days
Puberty
3
NA
13
742.0000
669.0000
615.000
24.000
26.0000
21.000
Mean
days
S.E.M
169.705627
183.847763
148.492424
NA
NA
NA
NA
50
50
50
Also data for mated females but havent included as would be a different environment (e.g. With males)
NA
NA
Rattus_norvegicus
longevity
16
17
Asdell et al 1967
Yes
Castration
Yes
Intact (no surgery)
No
Male
Rattus norvegicus
Rat
Cornell Nutrion colony
Laboratory
No
Between 39-42 days
Puberty
3
NA
14
615.0000
651.0000
742.000
21.000
26.0000
24.000
Mean
days
S.E.M
148.492424
183.847763
169.705627
NA
NA
NA
NA
50
50
50
NA
NA
NA
Rattus_norvegicus
longevity
17
18
Asdell and Joshi 1976
Yes
Ovariectomy
Yes
Intact (no surgery)
No
Female
Rattus norvegicus
Rat
Manor-Wistar
Laboratory
No
45 days old
Puberty
3
NA
15
654.0000
844.0000
661.000
24.000
24.0000
30.000
Mean
days
S.E.M
169.705627
169.705627
212.132034
NA
NA
NA
NA
50
50
50
NA
NA
NA
Rattus_norvegicus
longevity
18
19
Asdell and Joshi 1976
Yes
Castration
Yes
Intact (no surgery)
No
Male
Rattus norvegicus
Rat
Manor-Wistar
Laboratory
No
45 days old
Puberty
3
NA
16
661.0000
775.0000
654.000
30.000
30.0000
24.000
Mean
days
S.E.M
212.132034
212.132034
169.705627
NA
NA
NA
NA
50
50
50
NA
NA
NA
Rattus_norvegicus
longevity
19
20
Arriola Apelo et al 2020
Yes
Castration
Yes
Sham surgery
Yes
Male
Mus musculus
Mice
C57BL6
Laboratory
No
21 days
Prepuberty
2
NA
17
1006.0000
978.0000
853.000
34.300
37.4500
26.250
Median
days
S.E.M
145.522576
183.466782
136.399001
NA
NA
NA
NA
18
24
27
NA
NA
NA
Mus_musculus
longevity
20
21
Arriola Apelo et al 2020
Yes
Ovariectomy
Yes
Sham surgery
Yes
Female
Mus musculus
Mice
C57BL6
Laboratory
No
21 days
Prepuberty
2
NA
18
853.0000
916.0000
1006.000
26.250
49.3600
34.300
Median
days
S.E.M
136.399001
231.518922
145.522576
NA
NA
NA
NA
27
22
18
NA
NA
NA
Mus_musculus
longevity
21
22
Benedusi et al 2015
Yes
Castration
Yes
Sham surgery
Yes
Male
Mus musculus
Mice
C57BL76 (ERE-LucRepTOP™)
Laboratory
No
5 Months
Adult (old)
4
NA
19
0.1000
0.3500
NA
NA
NA
NA
Survival rate (%)
15 Months
NA
NA
NA
NA
NA
NA
NA
NA
20
20
NA
NA
NA
NA
Mus_musculus
mortality
22
24
Cargil et al 2003
Yes
Ovariectomy
Yes
Intact (no surgery)
No
Female
Mus musculus
Mice
CBA
Laboratory
No
21 days
Prepuberty
2
NA
21
599.2900
578.6400
NA
30.450
35.6000
NA
Median
Days
S.E.M
158.222841
178.000000
NA
NA
NA
NA
NA
27
25
NA
Extracted median lifespan from data in figure and calculated SD
NA
NA
Mus_musculus
longevity
23
25
Cox et al 2014
Yes
Ovariectomy
Yes
Sham surgery
Yes
Female
Anolis sagrei
Anole lizards
NA
Wild
Yes
Unknown - wild caught - assuming adult because caught at the same time of year as other studies, but work "adult" not specifically mentioned.
Adult
4
NA
22
0.2600
0.3300
NA
NA
NA
NA
Survival rate (%)
One breeding season (May-Sept)
NA
NA
NA
NA
NA
NA
NA
NA
168
170
NA
Raw data from Dyrad. My survival estimates from survival dont equal those extracted from the model, that probably includes covariates
NA
NA
Anolis_sagrei
mortality
24
26
Cox et al 2014
Yes
Ovariectomy
Yes
Sham surgery
Yes
Female
Anolis sagrei
Anole lizards
NA
Wild
Yes
Unknown - wild caught
Adult
4
NA
22
0.3000
0.2000
NA
NA
NA
NA
Survival rate (%)
Winter (Sept-May)
NA
NA
NA
NA
NA
NA
NA
NA
20
20
NA
Remaining animals from first mortality assessment and only controls where fat was not removed. My survival estimates from those alive after monitored period
NA
NA
Anolis_sagrei
mortality
25
27
Cox and Calsbeek 2010
Yes
Ovariectomy
Yes
Sham surgery
Yes
Female
Anolis sagrei
Anole lizards
NA
Wild
Yes
Unknown - wild caught
Adult
4
NA
23
0.0800
0.2400
NA
NA
NA
NA
Survival rate (%)
One year
NA
NA
NA
NA
NA
NA
NA
NA
188
194
NA
Data is also available for Summer and winter mortality, seperately
NA
NA
Anolis_sagrei
mortality
26
28
Cox et al 2010
Yes
Ovariectomy
Yes
Sham surgery
Yes
Female
Anolis sagrei
Anole lizards
NA
Wild
Yes
Adult - wild caught
Adult
4
NA
24
0.2300
0.3400
NA
NA
NA
NA
Survival rate (%)
One year
NA
NA
NA
NA
NA
NA
NA
NA
105
106
NA
NA
NA
NA
Anolis_sagrei
mortality
27
29
Reedy et al 2016
Yes
Ovariectomy
Yes
Sham surgery
Yes
Female
Anolis sagrei
Anole lizards
NA
Wild
Yes
Unknown - wild caught adult after start of breeding
Adult (young)
4
NA
25
0.2500
0.2100
0.550
NA
NA
NA
Survival rate (%)
10 weeks (of breeding season)
NA
NA
NA
NA
NA
NA
NA
NA
110
110
60
NA
NA
NA
Anolis_sagrei
mortality
28
30
Reedy et al 2016
Yes
Castration
Yes
Sham surgery
Yes
Male
Anolis sagrei
Anole lizards
NA
Wild
Yes
Unknown - wild caught adult after start of breeding
Adult (young)
4
NA
26
0.5500
0.2800
0.250
NA
NA
NA
Survival rate (%)
10 weeks (of breeding season)
NA
NA
NA
NA
NA
NA
NA
NA
60
60
110
NA
NA
NA
Anolis_sagrei
mortality
29
31
Drori and Folman 1976
Yes
Castration
Yes
Intact (no surgery)
No
Male
Rattus norvegicus
Rat
Albino
Laboratory
No
38-44 days. Stated as prepuberty
Prepuberty
2
NA
27
727.0000
817.0000
849.000
26.000
32.0000
26.000
Mean
Days
S.E.M
182.000000
224.000000
182.000000
NA
NA
NA
NA
49
49
49
Authors state that they castrated animals shortly before puberty, so coded as prepuberty
NA
NA
Rattus_norvegicus
longevity
30
32
Garratt et al 2021
Yes
Castration
Yes
Sham surgery
Yes
Male
Mus musculus
Mice
C57BL6
Laboratory
No
7-8 weeks
Adult (young)
4
NA
28
952.0000
960.0000
956.000
20.700
36.4000
28.500
Median
Days
S.E.M
117.096883
218.400000
156.100929
NA
NA
NA
NA
32
36
30
NA
NA
NA
Mus_musculus
longevity
31
33
Hamilton et al 1969
No
Castration
Yes
Intact (no surgery)
No
Male
Felis catus
Cats
Outbred
Domestic
No
Under 5 months (before sexual maturity)
Prepuberty
2
NA
29
5.3000
12.2000
7.700
0.420
1.4800
0.520
Mean
Years
S.E.M
4.136520
5.336216
4.794163
NA
NA
NA
NA
97
13
85
Correlative data is provided in a figure showing exact age at gonadectomy and lifespan for each individual
NA
NA
Felis_catus
longevity
32
34
Hamilton et al 1969
No
Castration
Yes
Intact (no surgery)
No
Male
Felis catus
Cats
Outbred
Domestic
No
6 to 7 months
Puberty
3
NA
29
5.3000
8.6000
7.700
0.420
1.1200
0.520
Mean
Years
S.E.M
4.136520
5.371331
4.794163
NA
NA
NA
NA
97
23
85
NA
NA
NA
Felis_catus
longevity
33
35
Hamilton et al 1969
No
Castration
Yes
Intact (no surgery)
No
Male
Felis catus
Cats
Outbred
Domestic
No
over 8 months
Adult
4
NA
29
5.3000
7.2000
7.700
0.420
0.7100
0.520
Mean
Years
S.E.M
4.136520
4.376734
4.794163
NA
NA
NA
NA
97
38
85
NA
NA
NA
Felis_catus
longevity
34
36
Hamilton et al 1969
No
Ovariectomy
Yes
Intact (no surgery)
No
Female
Felis catus
Cats
Outbred
Domestic
No
Various, median 6 months
NA
NA
NA
30
7.7000
8.2000
5.300
0.520
0.5200
0.420
Mean
Years
S.E.M
4.794163
4.503332
4.136520
NA
NA
NA
NA
85
75
97
NA
NA
NA
Felis_catus
longevity
35
37
Hamilton et al 1969
No
Ovariectomy
Yes
Intact (no surgery)
No
Female
Felis catus
Cats
Name breeds
Domestic
No
Various, median 6 months
NA
NA
NA
31
6.2000
8.2000
4.600
0.840
0.8100
0.700
Mean
Years
S.E.M
5.040000
4.723071
3.500000
NA
NA
NA
NA
36
34
25
NA
NA
NA
Felis_catus
longevity
36
38
Hamilton et al 1969
No
Castration
Yes
Intact (no surgery)
No
Male
Felis catus
Cats
Name breeds
Domestic
No
Various, median 6 months
NA
NA
NA
32
4.6000
6.9000
6.200
0.700
0.5900
0.840
Mean
Years
S.E.M
3.500000
4.971428
5.040000
NA
NA
NA
NA
25
71
36
NA
NA
NA
Felis_catus
longevity
37
39
Waters et al 2011
No
Ovariectomy
Yes
Intact (no surgery)
No
Female
Canis lupus
Dogs
Rottweilers
Domestic
No
6.1-8 years
Adult (old)
4
NA
33
0.2670
1.0000
NA
NA
NA
NA
Likelyhood of exceptional longevity
Survival to 13
NA
NA
NA
NA
NA
NA
NA
NA
14
14
NA
Look at whether animals were normally lived, or lived over 13 years. Author is providing me with the raw data
NA
NA
Canis_lupus
mortality
38
40
Waters et al 2011
No
Ovariectomy
Yes
Intact (no surgery)
No
Female
Canis lupus
Dogs
Rottweilers
Domestic
No
2.1-6 years
Adult (young)
4
NA
33
0.2670
0.4390
NA
NA
NA
NA
Likelyhood of exceptional longevity
Survival to 13
NA
NA
NA
NA
NA
NA
NA
NA
14
57
NA
Look at whether animals were normally lived, or lived over 13 years. Author is providing me with the raw data
NA
NA
Canis_lupus
mortality
39
41
Waters et al 2011
No
Ovariectomy
Yes
Intact (no surgery)
No
Female
Canis lupus
Dogs
Rottweilers
Domestic
No
0.4-2 years. Estimated as puberty because rott weilers start to go through heat at approximately 12-18 months
Puberty
3
NA
33
0.2670
0.3230
NA
NA
NA
NA
Likelyhood of exceptional longevity
Survival to 13
NA
NA
NA
NA
NA
NA
NA
NA
14
65
NA
Look at whether animals were normally lived, or lived over 13 years. Author is providing me with the raw data
NA
NA
Canis_lupus
mortality
40
42
Holland et al 1977
Yes
Ovariectomy
Yes
Intact (no surgery)
No
Female
Mus musculus
Mice
RFM
Laboratory
No
3-4 weeks
Prepuberty
2
NA
34
638.0000
628.0000
NA
16.000
16.0000
NA
Mean
Days
S.E.M
167.044904
162.382265
NA
NA
NA
NA
NA
109
103
NA
Just used data from non-irradiated group. Lots of pathology data
NA
NA
Mus_musculus
longevity
41
43
Kirkman and Yau 1972
No
Castration
Yes
Intact (no surgery)
No
Male
Mesocricetus auratus
Hamsters
Syrian Hamsters
Laboratory
No
Unknown - not given
NA
NA
NA
35
632.0000
508.0000
543.000
NA
NA
NA
Mean
Days
NA
222.910000
151.300000
222.950000
NA
NA
NA
NA
629
72
578
Do not have error presented in paper. The proportion of animals dying in 10 brackets of different ages is presented that could be used (Figs 3 and 4). Upper and low quartiles estimated from figure 3 when number of animals dying in 100 day periods is given. Have used the middle number within this period for the quartile value (e.g. 550-850 for intact males, 350-550 for castrated males)
NA
NA
Mesocricetus_auratus
longevity
42
44
Kirkman and Yau 1972
No
Ovariectomy
Yes
Intact (no surgery)
No
Female
Mesocricetus auratus
Hamsters
Syrian Hamsters
Laboratory
No
Unknown - not given
NA
NA
NA
36
543.0000
391.0000
632.000
NA
NA
NA
Mean
Days
NA
222.950000
155.440000
222.910000
NA
NA
NA
NA
578
31
629
Do not have error presented in paper. The proportion of animals dying in 10 brackets of different ages is presented that could be used (Figs 3 and 4). Upper and low quartiles estimated from figure 3 when number of animals dying in 100 day periods is given. Have used the middle number within this period for the quartile value (e.g. 450-750 for intact females, 250-450 for castrated females)
NA
NA
Mesocricetus_auratus
longevity
43
45
Sichuk 1965
Yes
Castration
Yes
Sham surgery
Yes
Male
Mesocricetus auratus
Hampsters
Syrian Hampsters
Laboratory
No
6 weeks
Puberty
3
NA
37
612.0000
578.0000
589.000
NA
NA
NA
Mean
Days
NA
222.910000
151.300000
222.950000
NA
NA
NA
NA
92
90
94
Use - SD from Kirkman & Yau;Error not provided. Mean lifespan comes from the lifespan of both those with thrumobis and those that do not have it. Calculated the mean of these two values weighed against the sample size of each group
NA
NA
Mesocricetus_auratus
longevity
44
46
Sichuk 1965
Yes
Ovariectomy
Yes
Sham surgery
Yes
Female
Mesocricetus auratus
Hampsters
Syrian Hampsters
Laboratory
No
6 weeks
Puberty
3
NA
38
589.0000
586.0000
612.000
NA
NA
NA
Mean
Days
NA
222.950000
155.440000
222.910000
NA
NA
NA
NA
94
92
92
Use - SD from Kirkman & Yau;Error not provided. Mean lifespan comes from the lifespan of both those with thrumobis and those that do not have it. Calculated the mean of these two values weighed against the sample size of each group
NA
NA
Mesocricetus_auratus
longevity
45
47
Mitchel et al 1999
No
Castration
Yes
Intact (no surgery)
No
Male
Canis lupus
Dogs
Various breeds
Domestic
No
Various
NA
NA
NA
39
131.0000
128.0000
130.000
1.400
2.7000
1.800
Mean
Months
S.E.M
50.029192
46.058550
51.951131
NA
NA
NA
NA
1277
291
833
Used all causes of death. Data collected on the basis of surveys that pet owners filled out about their last pets age at death
NA
NA
Canis_lupus
longevity
46
48
Mitchel et al 1999
No
Ovariectomy
Yes
Intact (no surgery)
No
Female
Canis lupus
Dogs
Various breeds
Domestic
No
Various
NA
NA
NA
40
130.0000
144.0000
131.000
1.800
1.5000
1.400
Mean
Months
S.E.M
51.951131
40.249224
50.029192
NA
NA
NA
NA
833
720
1277
Used all causes of death. Data collected on the basis of surveys that pet owners filled out about their last pets age at death
NA
NA
Canis_lupus
longevity
47
49
Moore et al 2001
No
Castration
Yes
Intact (no surgery)
No
Male
Canis lupus
Dogs
Military working dogs
Domestic
No
Various
NA
NA
NA
41
9.9700
10.4900
NA
2.100
2.0600
NA
Median
Years
Standard deviation
2.100000
2.060000
NA
NA
NA
NA
NA
641
143
NA
Shinichi made decisoin to halve the rest of N to control and the opposit sex;Do not know the sample size of castrated males. 641/927 animals in the study are intact males, the remaining animals are either castrated males or spayed females but we do not know sample size of each of these two groups.
NA
NA
Canis_lupus
longevity
48
50
Nieschlag et al 1993
No
Castration
Yes
Intact (no surgery)
No
Male
Homo sapiens
Humans
NA
NA
No
Castrate prepubertal boys to prevent maturation of voice
Prepuberty
2
NA
42
64.3000
65.5000
NA
14.100
13.8000
NA
Mean
Years
Standard Deviation
14.100000
13.800000
NA
NA
NA
NA
NA
200
50
NA
NA
NA
NA
Homo_sapiens
longevity
49
51
Slonaker 1930
Yes
Castration
Yes
Intact (no surgery)
No
Male
Rattus norvegicus
Rat
Albino
Laboratory
No
44 days. Testes had decended by the operation
Adult (young)
4
NA
43
788.0000
770.0000
863.000
22.250
28.0000
27.690
Mean
Days
Probable error
32.987398
41.512231
41.052632
NA
NA
NA
NA
10
8
17
NA
NA
NA
Rattus_norvegicus
longevity
50
53
Slonaker 1930
Yes
Ovariectomy
Yes
Intact (no surgery)
No
Female
Rattus norvegicus
Rat
Albino
Laboratory
No
27.5 days
Prepuberty
2
NA
44
863.0000
755.0000
788.000
27.690
22.1500
22.250
Mean
Days
Probable error
41.052632
32.839140
32.987398
NA
NA
NA
NA
17
37
10
NA
NA
NA
Rattus_norvegicus
longevity
51
54
Slonaker 1930
Yes
Hysterectomy
No
Intact (no surgery)
No
Female
Rattus norvegicus
Rat
Albino
Laboratory
No
29 days
Prepuberty
2
NA
44
863.0000
855.0000
788.000
27.690
12.6700
22.250
Mean
Days
Probable error
41.052632
18.784285
32.987398
NA
NA
NA
NA
17
60
10
NA
NA
NA
Rattus_norvegicus
longevity
52
55
Storer et al 1982
Yes
Ovariectomy
Yes
Intact (no surgery)
No
Female
Mus musculus
Mice
RFM
Laboratory
No
50 days
Adult (young)
4
NA
45
643.4000
662.2000
NA
5.910
7.3100
NA
Mean
Days
S.E.M.
161.311607
134.193762
NA
NA
NA
NA
NA
745
337
NA
Non-irradiated controls from an irradiation experiment
NA
NA
Mus_musculus
longevity
53
56
Storer et al 1982
Yes
Ovariectomy
Yes
Intact (no surgery)
No
Female
Mus musculus
Mice
Balb/c
Laboratory
No
50 days
Adult (young)
4
NA
46
762.9000
795.5000
NA
6.210
10.9500
NA
Mean
Days
S.E.M.
179.016109
197.707397
NA
NA
NA
NA
NA
831
326
NA
Non-irradiated controls from an irradiation experiment
NA
NA
Mus_musculus
longevity
54
57
Hoffman et al 2018
No
Castration
Yes
Intact (no surgery)
No
Male
Canis lupus
Dogs
Various breeds
Domestic
No
Various
NA
NA
NA
47
10.8600
11.6400
10.860
0.110
0.0700
0.140
Mean
Years
S.E.M
3.327386
2.033618
3.685485
NA
NA
NA
NA
915
844
693
Vetcompass database
NA
NA
Canis_lupus
longevity
55
58
Hoffman et al 2018
No
Ovariectomy
Yes
Intact (no surgery)
No
Female
Canis lupus
Dogs
Various breeds
Domestic
No
Various
NA
NA
NA
48
10.8600
12.1200
10.860
0.140
0.1900
0.110
Mean
Years
S.E.M
3.685485
5.766116
3.327386
NA
NA
NA
NA
693
921
915
Vetcompass database
NA
NA
Canis_lupus
longevity
56
59
Hoffman et al 2018
No
Castration
Yes
Intact (no surgery)
No
Male
Canis lupus
Dogs
Various breeds
Domestic
No
Various
NA
NA
NA
49
8.0000
9.2100
7.680
0.070
0.0400
0.070
Mean
Years
S.E.M
8.556337
4.241509
6.018372
NA
NA
NA
NA
14941
11244
7392
VMDB - individual data for breeds available in supplementary, but just mean lifespan without error
NA
NA
Canis_lupus
longevity
57
60
Hoffman et al 2018
No
Ovariectomy
Yes
Intact (no surgery)
No
Female
Canis lupus
Dogs
Various breeds
Domestic
No
Various
NA
NA
NA
50
7.6800
9.7300
8.000
0.070
0.0400
0.070
Mean
Years
S.E.M
6.018372
5.599714
8.556337
NA
NA
NA
NA
7392
19598
14941
VMDB - individual data for breeds available in supplementary, but just mean lifespan without error
NA
NA
Canis_lupus
longevity
58
61
Mason et al 2009
Yes
Ovariectomy
Yes
Intact (no surgery)
No
Female
Mus musculus
Mice
CBA
Laboratory
No
21 days
Prepuberty
2
NA
51
727.6000
715.0000
NA
15.900
20.0000
NA
Mean
Days
S.E.M
89.943983
101.980390
NA
NA
NA
NA
NA
32
26
NA
Worked out sample size from fig 4 This and the other entry for this paper have two different control comparisons
NA
NA
Mus_musculus
longevity
59
62
Mason et al 2009
Yes
Ovariectomy
Yes
Intact (no surgery)
No
Female
Mus musculus
Mice
CBA
Laboratory
No
21 days
Prepuberty
2
NA
51
725.6000
715.0000
NA
20.400
20.0000
NA
Mean
Days
S.E.M
117.189078
101.980390
NA
NA
NA
NA
NA
33
26
NA
Worked out sample size from fig 4
NA
NA
Mus_musculus
longevity
60
63
Talbert and Hamilton 1965
Yes
Castration
Yes
Sham surgery
Yes
Male
Rattus norvegicus
Rats
Lewis
Laboratory
No
Birth
Birth
1
NA
52
454.0000
521.0000
484.000
18.000
27.0000
19.000
Mean
Days
S.E.M
108.000000
174.979999
123.134073
NA
NA
NA
NA
36
42
42
NA
NA
NA
Rattus_norvegicus
longevity
61
64
Talbert and Hamilton 1965
Yes
Castration
Yes
Sham surgery
Yes
Male
Rattus norvegicus
Rats
Lewis
Laboratory
No
22-28 days
Prepuberty
2
NA
52
454.0000
488.0000
484.000
18.000
28.0000
19.000
Mean
Days
S.E.M
108.000000
165.650234
123.134073
NA
NA
NA
NA
36
35
42
NA
NA
NA
Rattus_norvegicus
longevity
62
65
Talbert and Hamilton 1965
Yes
Castration
Yes
Sham surgery
Yes
Male
Rattus norvegicus
Rats
Lewis
Laboratory
No
100 days
Adult (young)
4
NA
52
454.0000
439.0000
484.000
18.000
25.0000
19.000
Mean
Days
S.E.M
108.000000
119.895788
123.134073
NA
NA
NA
NA
36
23
42
NA
NA
NA
Rattus_norvegicus
longevity
63
66
Talbert and Hamilton 1965
Yes
Ovariectomy
Yes
Sham surgery
Yes
Female
Rattus norvegicus
Rats
Lewis
Laboratory
No
Birth
Birth
1
NA
53
484.0000
574.0000
454.000
19.000
33.0000
18.000
Mean
Days
S.E.M
123.134073
183.736224
108.000000
NA
NA
NA
NA
42
31
36
NA
NA
NA
Rattus_norvegicus
longevity
64
67
Talbert and Hamilton 1965
Yes
Ovariectomy
Yes
Sham surgery
Yes
Female
Rattus norvegicus
Rats
Lewis
Laboratory
No
22-28 days
Prepuberty
2
NA
53
484.0000
480.0000
454.000
19.000
44.0000
18.000
Mean
Days
S.E.M
123.134073
206.378293
108.000000
NA
NA
NA
NA
42
22
36
NA
NA
NA
Rattus_norvegicus
longevity
65
68
Talbert and Hamilton 1965
Yes
Ovariectomy
Yes
Sham surgery
Yes
Female
Rattus norvegicus
Rats
Lewis
Laboratory
No
100 days
Adult (young)
4
NA
53
484.0000
515.0000
454.000
19.000
41.0000
18.000
Mean
Days
S.E.M
123.134073
183.357574
108.000000
NA
NA
NA
NA
42
20
36
NA
NA
NA
Rattus_norvegicus
longevity
66
69
Tapprest et al 2017
No
Castration
Yes
Intact (no surgery)
No
Male
Equus ferus
Draught horse
NA
Farm
No
Unknown
Unknown
NA
NA
54
0.1520
0.1740
0.103
NA
NA
NA
Survival rate (%)
to 10 years
NA
NA
NA
NA
NA
NA
NA
NA
132
23
638
No error provided with median lifespan but there is proportion surviving to a specific age, which includes condfidence intervals, and survival curves. Have used survival to 10 yeays of age.
NA
NA
Equus_ferus
mortality
67
70
Tapprest et al 2017
No
Castration
Yes
Intact (no surgery)
No
Male
Equus ferus
Pony
NA
Farm
No
Unknown
Unknown
NA
NA
55
0.6970
0.6970
0.709
NA
NA
NA
Survival rate (%)
to 10 years
NA
NA
NA
NA
NA
NA
NA
NA
211
201
533
No error provided with median lifespan but there is proportion surviving to a specific age, which includes condfidence intervals, and survival curves. Have used survival to 10 yeays of age.
NA
NA
Equus_ferus
mortality
68
71
Tapprest et al 2017
No
Castration
Yes
Intact (no surgery)
No
Male
Equus ferus
Saddle horse
NA
Farm
No
Unknown
Unknown
NA
NA
56
0.5620
0.5540
0.575
NA
NA
NA
Survival rate (%)
to 10 years
NA
NA
NA
NA
NA
NA
NA
NA
1077
2203
4124
No error provided with median lifespan but there is proportion surviving to a specific age, which includes condfidence intervals, and survival curves. Have used survival to 10 yeays of age.
NA
NA
Equus_ferus
mortality
69
72
Hamilton 1965
No
Castration
Yes
Intact (no surgery)
No
Male
Felis catus
Cats
Various breeds
Domestic
No
6 -12 months for those that were known
Puberty or adult
NA
NA
57
3.2000
6.8000
7.700
0.340
0.5800
0.680
Mean
Years
S.E.M
2.741168
4.492661
5.178726
NA
NA
NA
NA
65
60
58
Error displayed must be standard error not standard deviation. I initially interpreted the symbol used as standard deviation but actually it must be SEM, and this is what Hamilton usually uses. Otherwise they are abnormally low.
NA
NA
Felis_catus
longevity
70
73
Hamilton 1965
No
Ovariectomy
Yes
Intact (no surgery)
No
Female
Felis catus
Cats
Various breeds
Domestic
No
6 -12 months for those that were known
Puberty or adult
NA
NA
58
7.7000
9.2000
3.200
0.680
0.8800
0.340
Mean
Years
S.E.M
5.178726
4.656522
2.741168
NA
NA
NA
NA
58
28
65
Error displayed must be standard error not standard deviation. I initially interpreted the symbol used as standard deviation but actually it must be SEM, and this is what Hamilton usually uses. Otherwise they are abnormally low.
NA
NA
Felis_catus
longevity
71
74
Hamilton 1965
No
Castration
Yes
Intact (no surgery)
No
Male
Felis catus
Cats
Various breeds
Domestic
No
6 -12 months for those that were known
Puberty or adult
NA
NA
59
6.1000
8.5000
7.400
0.660
0.5600
0.720
Mean
Years
S.E.M
4.713343
4.913980
5.091169
NA
NA
NA
NA
51
77
50
Error displayed must be standard error not standard deviation. I initially interpreted the symbol used as standard deviation but actually it must be SEM, and this is what Hamilton usually uses. Otherwise they are abnormally low.
NA
NA
Felis_catus
longevity
72
75
Hamilton 1965
No
Ovariectomy
Yes
Intact (no surgery)
No
Female
Felis catus
Cats
Various breeds
Domestic
No
6 -12 months for those that were known
Puberty or adult
NA
NA
60
7.4000
8.4000
6.100
0.720
0.7100
0.660
Mean
Years
S.E.M
5.091169
4.762825
4.713343
NA
NA
NA
NA
50
45
51
Error displayed must be standard error not standard deviation. I initially interpreted the symbol used as standard deviation but actually it must be SEM, and this is what Hamilton usually uses. Otherwise they are abnormally low.
NA
NA
Felis_catus
longevity
73
76
Huang et al 2017
No
Castration
Yes
Intact (no surgery)
No
Male
Canis lupus
Dogs
Various breeds
Domestic
No
Various
NA
NA
NA
61
9.0000
12.0000
10.000
NA
NA
NA
Median
Years
Interquartile range
5.941000
3.723000
5.947000
NA
NA
NA
NA
839
332
528
Interquartile range Intact, 5.0-13.0; castrated 9.0-14.0
NA
NA
Canis_lupus
longevity
74
77
Huang et al 2017
No
Ovariectomy
Yes
Intact (no surgery)
No
Female
Canis lupus
Dogs
Various breeds
Domestic
No
Various
NA
NA
NA
62
10.0000
12.0000
9.000
NA
NA
NA
Median
Years
Interquartile range
5.947000
3.938000
5.941000
NA
NA
NA
NA
528
607
839
Interquartile range Intact, 5.0-13.0; ovariectomy 9.7-15.0
NA
NA
Canis_lupus
longevity
75
78
Min et al 2012
No
Castration
Yes
Intact (no surgery)
No
Male
Homo sapiens
Humans
NA
NA
No
Various.The boys lost their reproductive organs in accidents, or they underwent deliberate castration to gain access to the palace before becoming a teenager.
Prepuberty
2
NA
63
55.6000
70.0000
NA
0.530
1.7600
NA
Median
Years
S.E.M
17.784639
15.840000
NA
NA
NA
NA
NA
1126
81
NA
Mok family
NA
NA
Homo_sapiens
longevity
76
79
Min et al 2012
No
Castration
Yes
Intact (no surgery)
No
Male
Homo sapiens
Humans
NA
NA
No
Various.The boys lost their reproductive organs in accidents, or they underwent deliberate castration to gain access to the palace before becoming a teenager.
Prepuberty
2
NA
63
52.9000
70.0000
NA
0.450
1.7600
NA
Median
Years
S.E.M
16.921436
15.840000
NA
NA
NA
NA
NA
1414
81
NA
Shin family
NA
NA
Homo_sapiens
longevity
77
80
Min et al 2012
No
Castration
Yes
Intact (no surgery)
No
Male
Homo sapiens
Humans
NA
NA
No
Various.The boys lost their reproductive organs in accidents, or they underwent deliberate castration to gain access to the palace before becoming a teenager.
Orongorono 50% sterility_Survival from Fig 7, sample sizes from number of animals released over the years 1996-1999
NA
NA
Trichosurus_vulpecula
mortality
87
90
Ramsey 2005
Yes
Tubul-ligation
No
Sham surgery
Yes
Female
Trichosurus vulpecula
Possum
NA
Wild
Yes
Various
Adult
4
NA
71
0.7200
0.8300
NA
NA
NA
NA
Survival rate (%)
Annual (taken from 4 years)
NA
NA
NA
NA
NA
NA
NA
NA
36
142
NA
Orongorono 80% sterility_Survival from Fig 7, sample sizes from number of animals released over the years 1996-1999
NA
NA
Trichosurus_vulpecula
mortality
88
91
Ramsey 2005
Yes
Tubul-ligation
No
Sham surgery
Yes
Female
Trichosurus vulpecula
Possum
NA
Wild
Yes
Various
Adult
4
NA
72
0.6300
0.7600
NA
NA
NA
NA
Survival rate (%)
Annual (taken from 4 years)
NA
NA
NA
NA
NA
NA
NA
NA
215
215
NA
Turitea 50% sterility_Survival from Fig 7, sample sizes from number of animals released over the years 1996-1999. Confidence intervals are provided
NA
NA
Trichosurus_vulpecula
mortality
89
92
Ramsey 2005
Yes
Tubul-ligation
No
Sham surgery
Yes
Female
Trichosurus vulpecula
Possum
NA
Wild
Yes
Various
Adult
4
NA
73
0.6000
0.7400
NA
NA
NA
NA
Survival rate (%)
Annual (taken from 4 years)
NA
NA
NA
NA
NA
NA
NA
NA
31
123
NA
Turitea 80% sterility_Survival from Fig 7, sample sizes from number of animals released over the years 1996-1999
NA
NA
Trichosurus_vulpecula
mortality
90
93
Ramsey et al 2021
Yes
levonorgestrel implant
No
Sham capture
Yes
Female
Phascolarctos cinereus
Koala
NA
Wild
Yes
Mature females, as definted by toothwear, under 1 year
Adult (young)
4
NA
74
0.7200
0.7800
NA
NA
NA
NA
Survival rate (%)
Annual (average from across all years)
NA
NA
NA
NA
NA
NA
NA
NA
603
4355
NA
Survival rate taken as the average across all years. Sample size is also from across all years. Yearly data is also available in supplementary. Confidence intervals are provided.
NA
NA
Phascolarctos_cinereus
mortality
91
94
Muhlock 1959
Yes
Castration
Yes
Intact (no surgery)
No
Male
Mus musculus
Mouse
DBA
Laboratory
No
Weaning (1month)
Prepuberty
2
NA
75
578.0000
595.0000
667.000
10.120
11.4400
9.570
Mean
days
S.E.M
78.389183
102.322471
88.748529
NA
NA
NA
NA
60
80
86
Extracted data and calculated mean and SE from graph
NA
NA
Mus_musculus
longevity
92
95
Muhlock 1959
Yes
Ovariectomy
Yes
Intact (no surgery)
No
Female
Mus musculus
Mouse
DBA
Laboratory
No
Weaning (1 month)
Prepuberty
2
NA
76
667.0000
627.0000
578.000
9.570
10.6700
10.120
Mean
days
S.E.M
88.748529
87.987074
78.389183
NA
NA
NA
NA
86
68
60
Extracted data and calculated mean and SE from graph
NA
NA
Mus_musculus
longevity
93
96
Jewel 1997
Yes
Castration
Yes
Intact (no surgery)
No
Male
Ovis aries
Sheep
Soay sheep
Wild
Yes
Lambs
Birth
1
NA
77
0.3600
0.7100
0.410
NA
NA
NA
Survival rate (%)
one year
NA
NA
NA
NA
NA
NA
NA
NA
14
14
54
1978 Calaculated the survival rate to the timepoint nearest 50% intact male survival
NA
NA
Ovis_aries
mortality
94
97
Jewel 1997
Yes
Castration
Yes
Intact (no surgery)
No
Male
Ovis aries
Sheep
Soay sheep
Wild
Yes
Lambs
Birth
1
NA
78
0.2000
0.8800
0.910
NA
NA
NA
Survival rate (%)
One year
NA
NA
NA
NA
NA
NA
NA
NA
8
5
44
1979
NA
NA
Ovis_aries
mortality
95
98
Jewel 1997
Yes
Castration
Yes
Intact (no surgery)
No
Male
Ovis aries
Sheep
Soay sheep
Wild
Yes
Lambs
Birth
1
NA
79
0.0800
0.6600
0.400
NA
NA
NA
Survival rate (%)
Five years
NA
NA
NA
NA
NA
NA
NA
NA
50
50
83
1980. Calculated the survival rate to timepoint nearest to 50% intact male survival, and where there is data for all groups. A survival curve is also available but there is alot of missing data
NA
NA
Ovis_aries
mortality
96
99
Iwasa et al 2018
Yes
Ovariectomy
Yes
Sham surgery
Yes
Female
Rattus norvegicus
Rats
Sprague-Dawley
Laboratory
No
23 weeks
Late adult
4
NA
80
0.4300
0.8600
NA
NA
NA
NA
Survival rate (%)
~85 weeks.
NA
NA
NA
NA
NA
NA
NA
NA
8
7
NA
Calculated from a partial survival curve. Looked at when 50% of the control group died and then the number alive in the treatment group at this point
NA
NA
Rattus_norvegicus
mortality
97
100
Hamilton and Mestler 1969
No
Castration
Yes
Intact (no surgery)
No
Male
Homo sapiens
Humans
Mentally handicaped individuals
NA
No
8-14 years (prepubertal)
Prepuberty
2
NA
81
64.7000
76.3000
NA
0.990
1.3600
NA
Median lifespan (for those alive at 40)
Years
S.E.M
17.709658
5.769991
NA
NA
NA
NA
NA
320
18
NA
Median lifespan data from Table 10. This data is taken from those individuals alive at 40. Data for those alive at earlier ages is also available but not stratified into different surgery ages
NA
NA
Homo_sapiens
longevity
98
101
Hamilton and Mestler 1969
No
Castration
Yes
Intact (no surgery)
No
Male
Homo sapiens
Humans
Mentally handicaped individuals
NA
No
15-19 years
Adult (young)
4
NA
81
64.7000
72.9000
NA
0.990
5.1300
NA
Median lifespan (for those alive at 40)
Years
S.E.M
17.709658
43.529493
NA
NA
NA
NA
NA
320
72
NA
Median lifespan data from Table 10. This data is taken from those individuals alive at 40. Data for those alive at earlier ages is also available but not stratified into different surgery ages
NA
NA
Homo_sapiens
longevity
99
102
Hamilton and Mestler 1969
No
Castration
Yes
Intact (no surgery)
No
Male
Homo sapiens
Humans
Mentally handicaped individuals
NA
No
20-29 years
Adult (young)
4
NA
81
64.7000
69.6000
NA
0.990
2.5000
NA
Median lifespan (for those alive at 40)
Years
S.E.M
17.709658
19.525624
NA
NA
NA
NA
NA
320
61
NA
Median lifespan data from Table 10. This data is taken from those individuals alive at 40. Data for those alive at earlier ages is also available but not stratified into different surgery ages
NA
NA
Homo_sapiens
longevity
100
103
Hamilton and Mestler 1969
No
Castration
Yes
Intact (no surgery)
No
Male
Homo sapiens
Humans
Mentally handicaped individuals
NA
No
30-39 years
Adult (old)
4
NA
81
64.7000
68.9000
NA
0.990
2.0500
NA
Median lifespan (for those alive at 40)
Years
S.E.M
17.709658
14.350000
NA
NA
NA
NA
NA
320
49
NA
Median lifespan data from Table 10. This data is taken from those individuals alive at 40. Data for those alive at earlier ages is also available but not stratified into different surgery ages
NA
NA
Homo_sapiens
longevity
101
104
Hamilton and Mestler 1969
No
Ovariectomy
Yes
Intact (no surgery)
No
Female
Homo sapiens
Humans
Mentally handicaped individuals
NA
No
13-46 years old
Adult
4
NA
82
33.9000
56.2000
NA
1.360
4.6900
NA
Median
Years
S.E.M
15.265962
15.554970
NA
NA
NA
NA
NA
126
11
NA
Only one female was 13 years and all the rest were clearly adult so group coded as adult
NA
NA
Homo_sapiens
longevity
102
105
Oneil et al 2013
No
Castration
Yes
Intact (no surgery)
No
Male
Canis lupus
Dogs
Various breeds
Domestic
No
Various
NA
NA
NA
83
11.9900
11.9900
11.590
NA
NA
NA
Mean
Years
Estimated
NA
NA
NA
0.8
0.500
1.1000
Average difference in years to control
1464
1224
1082
Means calculated from the coefficient change in lifespan in Table 4. Sample size for all was known, SD was estimated
NA
NA
Canis_lupus
longevity
103
106
Oneil et al 2013
No
Ovariectomy
Yes
Intact (no surgery)
No
Female
Canis lupus
Dogs
Various breeds
Domestic
No
Various
NA
NA
NA
84
11.5900
12.3900
11.990
NA
NA
NA
Mean
Years
Estimated
NA
NA
NA
Look at comments for Coefficent whiich is in relation to the control female from the same study)
NA
NA
NA
1082
1304
1464
Means calculated from the coefficient change in lifespan in Table 4. Sample size for all was known, SD was estimated
NA
NA
Canis_lupus
longevity
104
107
Oneil et al 2015
No
Castration
Yes
Intact (no surgery)
No
Male
Felis catus
Cats
Various breeds
Domestic
No
Various
NA
NA
NA
85
12.8100
14.7100
14.610
NA
NA
NA
Mean
Years
Estimated
NA
NA
NA
0.6
0.100
1.0000
Average difference in years to control
704
1296
707
Means calculated from the coefficient change in lifespan in Table 4. Sample size for each sex was estimated on the basis of the total sample size and the percentage that were known to be sterilized in both sexes, SD was estimated
NA
NA
Felis_catus
longevity
105
108
Oneil et al 2015
No
Ovariectomy
Yes
Intact (no surgery)
No
Female
Felis catus
Cats
Various breeds
Domestic
No
Various
NA
NA
NA
86
14.6100
15.2100
12.810
NA
NA
NA
Mean
Years
Estimated
NA
NA
NA
Look at comments for Coefficent whiich is in relation to the control female from the same study)
NA
NA
NA
707
1302
704
Means calculated from the coefficient change in lifespan in Table 4. Sample size for each sex was estimated on the basis of the total sample size and the percentage that were known to be sterilized in both sexes, SD was estimated
NA
NA
Felis_catus
longevity
106
112
Wilson et al 2019
No
Ovariectomy
Yes
Intact (no surgery)
No
Female
Homo sapiens
Humans
NA
NA
No
Under 50 years old
Adult
4
NA
90
0.9351
0.9166
NA
NA
NA
NA
Survival rate (%)
21.5 years (median follow-up)
NA
NA
NA
NA
NA
NA
NA
NA
10218
851
NA
Calculated survival rate from those surviving across the study period. Hazard ratios are also provided in the paper, and additional analysis cotrolling for factors. There is also analysis where women are seperated according to whether they have used hormone replacement therapy. Additional studies are cited that have conducted this type of analysis.
NA
NA
Homo_sapiens
mortality
107
113
Wilson et al 2019
No
Hysterectomy
No
Intact (no surgery)
No
Female
Homo sapiens
Humans
NA
NA
No
Under 50 years old
Adult
4
NA
90
0.9351
0.9324
NA
NA
NA
NA
Survival rate (%)
21.5 years (median follow-up)
NA
NA
NA
NA
NA
NA
NA
NA
10218
2472
NA
Calculated survival rate from those surviving across the study period. Hazard ratios are also provided in the paper, and additional analysis cotrolling for factors. There is also analysis where women are seperated according to whether they have used hormone replacement therapy. Additional studies are cited that have conducted this type of analysis.
NA
NA
Homo_sapiens
mortality
108
114
Cheng 2019
Yes
Castration
Yes
Sham surgery
Yes
Male
Mus musculus
Mice
UMHet3
Laboratory
No
Under 30 days is stated. The figure shows weights starting at approximately 15-20 days so this is used in the correlation analysis
Prepuberty
2
NA
91
0.8100
0.9700
NA
NA
NA
NA
Survival rate (%)
500 days
NA
NA
NA
NA
NA
NA
NA
NA
238
238
NA
NA
NA
NA
Mus_musculus
mortality
109
115
Bronson 1981
No
Castration
Yes
Intact (no surgery)
No
Male
Felis catus
Cats
Various breeds
Domestic
No
After 6 months (they state few were done before neutered before 6 months or so)
Puberty or adult
NA
NA
92
4.9700
7.3400
6.650
3.660
4.2900
5.660
Mean
Years - for cats surviving to two years old
Standard deviation
3.660000
4.290000
5.660000
NA
NA
NA
NA
219
265
99
Lifespan of individuals calculated from histograms. Used data from animals that survived after the first year.
NA
NA
Felis_catus
longevity
110
116
Bronson 1981
No
Ovariectomy
Yes
Intact (no surgery)
No
Female
Felis catus
Cats
Various breeds
Domestic
No
After 6 months (they state few were done before neutered before 6 months or so)
Puberty or adult
NA
NA
93
6.6500
9.1100
4.970
5.660
5.1300
3.660
Mean
Years - for cats surviving to two years old
Standard deviation
5.660000
5.130000
3.660000
NA
NA
NA
NA
99
220
219
Lifespan of individuals calculated from histograms. Used data from animals that survived after the first year.
NA
NA
Felis_catus
longevity
111
117
Bronson 1982
No
Castration
Yes
Intact (no surgery)
No
Male
Canis lupus
Dogs
Various breeds
Domestic
No
Various
NA
NA
NA
94
8.0000
9.9000
7.700
5.200
6.8000
4.400
Mean (from those alive after 2)
Years
Standard Deviation
5.200000
6.800000
4.400000
NA
NA
NA
NA
755
54
224
Mean lifespan is that of those that are alive from 2 years of age. The authors also provide lifespan from birth, but point out that animals are not usually sterilized until at least 6 months, so this doesnt provide a fair comparison
NA
NA
Canis_lupus
longevity
112
118
Bronson 1982
No
Ovariectomy
Yes
Intact (no surgery)
No
Female
Canis lupus
Dogs
Various breeds
Domestic
No
Various
NA
NA
NA
95
7.7000
8.8000
8.000
4.400
6.9000
5.200
Mean (from those alive after 2)
Years
Standard Deviation
4.400000
6.900000
5.200000
NA
NA
NA
NA
224
528
755
Mean lifespan is that of those that are alive from 2 years of age. The authors also provide lifespan from birth, but point out that animals are not usually sterilized until at least 6 months, so this doesnt provide a fair comparison
NA
NA
Canis_lupus
longevity
113
119
Cox et al 2021
Yes
Ovariectomy
Yes
Sham surgery
Yes
Female
Anolis sagrei
Anole lizards
NA
Site FP predation natural
Yes
Unknown
NA
NA
NA
96
0.3590
0.5250
NA
NA
NA
NA
Survival rate (%)
One breeding season (May-Sept)
NA
NA
NA
NA
NA
NA
NA
NA
78
80
NA
Site FP predation natural
NA
NA
Anolis_sagrei
mortality
114
120
Cox et al 2021
Yes
Ovariectomy
Yes
Sham surgery
Yes
Female
Anolis sagrei
Anole lizards
NA
Site FC no predation
No
Unknown
NA
NA
NA
97
0.5430
0.3590
NA
NA
NA
NA
Survival rate (%)
One breeding season (May-Sept)
NA
NA
NA
NA
NA
NA
NA
NA
81
78
NA
Site FC no predation. Included as not wild-semi wild because protected from predation
NA
NA
Anolis_sagrei
mortality
115
121
Cox et al 2021
Yes
Ovariectomy
Yes
Sham surgery
Yes
Female
Anolis sagrei
Anole lizards
NA
Site HC Bird predation
Yes
Unknown
NA
NA
NA
98
0.3210
0.3920
NA
NA
NA
NA
Survival rate (%)
One breeding season (May-Sept)
NA
NA
NA
NA
NA
NA
NA
NA
81
79
NA
Site HC Bird predation
NA
NA
Anolis_sagrei
mortality
116
122
Cox et al 2021
Yes
Ovariectomy
Yes
Sham surgery
Yes
Female
Anolis sagrei
Anole lizards
NA
Site FP Natural
Yes
Unknown
NA
NA
NA
99
0.3270
0.5610
NA
NA
NA
NA
Survival rate (%)
One breeding season (May-Sept)
NA
NA
NA
NA
NA
NA
NA
NA
110
114
NA
Site FP Natural
NA
NA
Anolis_sagrei
mortality
117
123
Cox et al 2021
Yes
Ovariectomy
Yes
Sham surgery
Yes
Female
Anolis sagrei
Anole lizards
NA
Site NC No predation
No
Unknown
NA
NA
NA
100
0.2930
0.3470
NA
NA
NA
NA
Survival rate (%)
One breeding season (May-Sept)
NA
NA
NA
NA
NA
NA
NA
NA
75
75
NA
Site NC No predation. Included as not wild semi-wild because protected from predation
NA
NA
Anolis_sagrei
mortality
118
124
Cox et al 2021
Yes
Ovariectomy
Yes
Sham surgery
Yes
Female
Anolis sagrei
Anole lizards
NA
Site FC Bird predation
Yes
Unknown
NA
NA
NA
101
0.4930
0.5730
NA
NA
NA
NA
Survival rate (%)
One breeding season (May-Sept)
NA
NA
NA
NA
NA
NA
NA
NA
75
75
NA
Site FC Bird predation
NA
NA
Anolis_sagrei
mortality
119
125
Cox et al 2021
Yes
Ovariectomy
Yes
Sham surgery
Yes
Female
Anolis sagrei
Anole lizards
NA
Site HC Bird and snake predation
Yes
Unknown
NA
NA
NA
102
0.3330
0.2840
NA
NA
NA
NA
Survival rate (%)
One breeding season (May-Sept)
NA
NA
NA
NA
NA
NA
NA
NA
74
75
NA
Site HC Bird and snake predation
NA
NA
Anolis_sagrei
mortality
120
126
Cox et al 2021
Yes
Ovariectomy
Yes
Sham surgery
Yes
Female
Anolis sagrei
Anole lizards
NA
Site Mc Bird and snake predation
Yes
Unknown
NA
NA
NA
103
0.2670
0.2530
NA
NA
NA
NA
Survival rate (%)
One breeding season (May-Sept)
NA
NA
NA
NA
NA
NA
NA
NA
75
75
NA
Site Mc Bird and snake predation
NA
NA
Anolis_sagrei
mortality
121
127
Cox et al 2021
Yes
Ovariectomy
Yes
Sham surgery
Yes
Female
Anolis sagrei
Anole lizards
NA
Site FP natural
Yes
Unknown
NA
NA
NA
104
0.2290
0.3400
NA
NA
NA
NA
Survival rate (%)
One breeding season (May-Sept)
NA
NA
NA
NA
NA
NA
NA
NA
105
106
NA
Site FP natural
NA
NA
Anolis_sagrei
mortality
122
128
Skinner 2007
Yes
Tubul-ligation
No
Anesthetized but no surgery
Yes
Female
Odocoileus virginianus
White-tailed deer
NA
Suburban chicago
No
Unknown
NA
NA
NA
105
0.7500
0.5200
0.660
NA
NA
NA
Survival rate (%)
four years
NA
NA
NA
NA
NA
NA
NA
NA
34
67
79
It is stated that more treatment does died from vechicle accidents (e.g. Human biased)
NA
NA
Odocoileus_virginianus
mortality
123
129
Kent et al 2018
No
Castration
Yes
Intact (no surgery)
No
Male
Canis lupus
Dogs
Golden retriver
Domestic
No
Unknown
NA
NA
NA
106
8.6800
9.3500
5.890
NA
NA
NA
Median
Years
Range
3.230000
2.710000
3.270000
NA
NA
NA
NA
118
228
58
Standard deviation calculated from the range according to Wan et al 2014
NA
NA
Canis_lupus
longevity
124
130
Kent et al 2018
No
Ovariectomy
Yes
Intact (no surgery)
No
Female
Canis lupus
Dogs
Golden retriver
Domestic
No
Unknown
NA
NA
NA
107
5.8900
9.5100
8.680
NA
NA
NA
Median
Years
Range
3.270000
2.740000
3.230000
NA
NA
NA
NA
58
248
118
Standard deviation calculated from the range according to Wan et al 2014
NA
NA
Canis_lupus
longevity
125
131
Iversen et al 2007
No
Tubul-ligation
No
Intact (no surgery)
No
Female
Homo sapiens
Humans
NA
NA
No
Various
NA
NA
NA
108
0.9195
0.9124
NA
NA
NA
NA
Survival rate (%)
1968-2004 approx
NA
NA
NA
NA
NA
NA
NA
NA
2634
2511
NA
Groups differ in some demographic factors ie parity. Used data from Table 3 (all cause dealth), where women who had a history of cancer or cardiovascular disease etc, before their operation or follow-up period, were excluded from the analysis (see statistics section).
NA
NA
Homo_sapiens
mortality
126
132
Sato et al 1997
Yes
Ovariectomy
Yes
Sham surgery
Yes
Female
Rattus norvegicus
Rats
Sprague-Dawley
Laboratory
No
9 months
Adult
4
NA
109
0.8000
0.9710
NA
NA
NA
NA
Survival rate (%)
6 months
NA
NA
NA
NA
NA
NA
NA
NA
35
35
NA
NA
NA
NA
Rattus_norvegicus
mortality
127
133
Aida et al 1984
Yes
Castration
Yes
Sham surgery
Yes
Male
Oncorhynchus masou
masu salmon
NA
Laboratory
No
precocious
NA
NA
NA
110
0.3360
0.6200
NA
NA
NA
NA
Survival rate (%)
8 months
NA
NA
NA
NA
NA
NA
NA
NA
107
316
NA
Experiments were conducted on precocious mature males. Need to work out how to classify this in terms of maturity at castration. Some males had partial gonads remaining at
NA
NA
Oncorhynchus_masou
mortality
128
135
Pullinger and Head 1964
Yes
Ovariectomy
Yes
Untreated
No
Female
Mus musculus
Mice
C3Hf
Laboratory
No
56-111 days of age
Adult
4
NA
112
0.3700
0.2300
NA
NA
NA
NA
Survival rate (%)
to 30 months
NA
NA
NA
NA
NA
NA
NA
NA
114
40
NA
"average lifespan" in months is also provided but no error. Calculated the percentage surviving to 30 months as dead ranges are provided in 6 brackets and this is the closest to the median for control females. This group is control virgin females (OVX data was shared and compared to breeding females in the other female comparison for this paper but I removed it because I dont think social environment was comparable)
NA
NA
Mus_musculus
mortality
129
136
Robertson et al 1961
No
Castration
Yes
Untreated
No
Male
Oncorhynchus nerka
Kokanee salmon
NA
Experimental pond
No
2 years 1 month
Prior to sexual maturity
2
NA
113
4.0500
5.3100
4.260
NA
NA
NA
Mean
NA
NA
0.590000
1.600000
0.550000
NA
NA
NA
NA
41
13
58
Only used data on animals that were found dead and did not have regenerating gonads. Mortality data is presented for a larger cohort that were also exposed to predation but control data is not seperated according to sex.
NA
NA
Oncorhynchus_nerka
longevity
130
137
Robertson et al 1961
No
Ovariectomy
Yes
Untreated
No
Female
Oncorhynchus nerka
Kokanee salmon
NA
Experimental pond
No
2 years 1 month
Prior to sexual maturity
2
NA
114
4.2600
5.8900
4.050
NA
NA
NA
Mean
NA
NA
0.550000
1.630000
0.590000
NA
NA
NA
NA
58
16
41
Only used data on animals that were found dead and did not have regenerating gonads. Mortality data is presented for a larger cohort that were also exposed to predation but control data is not seperated according to sex.
NA
NA
Oncorhynchus_nerka
longevity
131
138
Saunders et al 2002
Yes
Tubul-ligation
No
Intact (no surgery)
No
Female
Vulpes vulpes
Foxes
NA
Wild
Yes
Adult (toothwear)
NA
4
NA
115
0.8300
0.8500
0.885
NA
NA
NA
Survival rate (%)
2 years
NA
NA
NA
NA
NA
NA
NA
NA
6
20
26
Included as controled because comparison is to animals on the same site. Male data is also from the same site
NA
NA
Vulpes_vulpes
mortality
132
139
Saunders et al 2002
No
Tubul-ligation
No
Sham surgery
Yes
Female
Vulpes vulpes
Foxes
NA
Wild
Yes
Adult (toothwear)
NA
4
NA
115
0.7860
0.8500
0.955
NA
NA
NA
Survival rate (%)
2 years
NA
NA
NA
NA
NA
NA
NA
NA
14
20
22
Included as not controlled as effect of sham surgery was assessed on a neighouring site although the authors compare survivalship rates between
NA
NA
Vulpes_vulpes
mortality
133
140
Collins and Kasbohn 2017
No
Ovariectomy
Yes
untreated
No
Female
Equus ferus
Feral horse
NA
Wild
Yes
Adult
NA
4
NA
116
0.8610
0.8510
0.900
NA
NA
NA
Survival rate (%)
Annual
NA
NA
NA
NA
NA
NA
NA
NA
114
36
10
There is also male data but they used a lot of different methods, mainly vasectomy and chemical castration, and data is not split according to surgery type
NA
NA
Equus_ferus
mortality
134
141
Urfer et al 2020
No
Castration
Yes
Intact (no surgery)
No
Male
Canis lupus
Dogs
NA
Various
No
Various
NA
NA
NA
117
15.0000
15.2000
14.100
NA
NA
NA
Median survival time
Years
Confidence interval (calcualted SD)
17.598000
16.530000
20.090000
NA
NA
NA
NA
2115
8567
1551
Calculated SD looks high? Another MSL from age 5 years is provided by not sure of the sample size included
NA
NA
Canis_lupus
longevity
135
142
Urfer et al 2020
No
Ovariectomy
Yes
Intact (no surgery)
No
Male
Canis lupus
Dogs
NA
Various
No
Various
NA
NA
NA
118
14.1000
15.8000
15.000
NA
NA
NA
Median survival time
Years
Confidence interval (calcualted SD)
20.090000
16.670000
17.598000
NA
NA
NA
NA
1551
8711
2115
Calculated SD looks high? Another MSL from age 5 years is provided by not sure of the sample size included
NA
NA
Canis_lupus
longevity
136
143
Ossewaarde et al 2005
No
Hysterectomy
No
Intact (no surgery)
No
Female
Homo sapiens
Humans
NA
NA
No
Various
NA
NA
NA
119
0.7825
0.8197
NA
NA
NA
NA
Survival rate (%)
Mean 17 years
NA
NA
NA
NA
NA
NA
NA
NA
10087
743
NA
Taken from number of cases of mortality across the follow-up period (Table 3)
NA
NA
Homo_sapiens
mortality
137
144
Ossewaarde et al 2005
No
Ovariectomy
Yes
Intact (no surgery)
No
Female
Homo sapiens
Humans
NA
NA
No
Various
NA
NA
NA
119
0.7825
0.8139
NA
NA
NA
NA
Survival rate (%)
Mean 17 years
NA
NA
NA
NA
NA
NA
NA
NA
10087
865
NA
Taken from number of cases of mortality across the follow-up period (Table 3)
NA
NA
Homo_sapiens
mortality
138
145
Hotchkiss 1995
Yes
Ovariectomy
Yes
Sham surgery
Yes
Female
Rattus norvegicus
Rats
Sprague-Dawley
Laboratory
No
90 days
Adult
4
NA
120
0.3330
0.8330
NA
NA
NA
NA
Survival rate (%)
to 630 days
NA
NA
NA
NA
NA
NA
NA
NA
12
12
NA
Survival to 630 days. Animals that presented with subquaneous tumors had these surgically removed.
NA
NA
Rattus_norvegicus
mortality
139
146
Rocca et al 2006
No
Ovariectomy
Yes
Intact (no surgery)
No
Female
Homo sapiens
Humans
NA
NA
No
Under 45 years old
Adult
4
NA
121
0.8380
0.7340
NA
NA
NA
NA
Survival rate (%)
Not sure, but total follow up years is provided in tables
NA
NA
NA
NA
NA
NA
NA
NA
1417
124
NA
Data from Table 1 and includes all individuals in that bracket
NA
NA
Homo_sapiens
mortality
140
147
Rocca et al 2006
No
Ovariectomy
Yes
Intact (no surgery)
No
Female
Homo sapiens
Humans
NA
NA
No
NA
Adult
4
NA
122
0.6280
0.6910
NA
NA
NA
NA
Survival rate (%)
Not sure, but total follow up years is provided in tables
NA
NA
NA
NA
NA
NA
NA
NA
645
243
NA
Data from Table 1 and includes all individuals in that bracket
NA
NA
Homo_sapiens
mortality
141
148
Rocca et al 2006
No
Ovariectomy
Yes
Intact (no surgery)
No
Female
Homo sapiens
Humans
NA
NA
No
NA
Adult
4
NA
123
0.5050
0.5530
NA
NA
NA
NA
Survival rate (%)
Not sure, but total follow up years is provided in tables
NA
NA
NA
NA
NA
NA
NA
NA
321
170
NA
Data from Table 1 and includes all individuals in that bracket
NA
NA
Homo_sapiens
mortality
142
149
Howard et al 2005
No
Hysterectomy
No
Intact (no surgery)
No
Female
Homo sapiens
Humans
NA
NA
No
NA
Adult
4
NA
124
0.9790
0.9770
NA
NA
NA
NA
Survival rate (%)
NA
NA
NA
NA
NA
NA
NA
NA
NA
52976
18687
NA
Total sample size split between groups is shown in "age at screening", which equates to the total sample size given at start of results. Calculated mortality by adding all causes together. Not sure have calculated this correctly or why it differs so dramatically from % annual that is given
NA
NA
Homo_sapiens
mortality
143
150
Howard et al 2005
No
Ovariectomy
Yes
Intact (no surgery)
No
Female
Homo sapiens
Humans
NA
NA
No
NA
Adult
4
NA
124
0.9790
0.9760
NA
NA
NA
NA
Survival rate (%)
NA
NA
NA
NA
NA
NA
NA
NA
NA
52976
18251
NA
Total sample size split between groups is shown in "age at screening", which equates to the total sample size given at start of results. Calculated mortality by adding all causes together. Not sure have calculated this correctly or why it differs so dramatically from % annual that is given
NA
NA
Homo_sapiens
mortality
144
151
Phelan 1995
Yes
Ovariectomy
Yes
Sham surgery
Yes
Female
Mus musculus
Mice
Swiss
Laboratory
No
Weaning
Weaning
2
NA
125
0.4100
0.6200
NA
NA
NA
NA
Survival rate (%)
To 800 days
NA
NA
NA
NA
NA
NA
NA
NA
30
30
NA
Animals were maintained on a 90% of adlibitum diet. This was a control group for a sepeate CR study and they state stopped the animals getting fat.
NA
NA
Mus_musculus
mortality
145
152
Manson et al 2013
No
Hysterectomy
No
Intact (no surgery)
No
Female
Homo sapiens
Humans
NA
NA
No
Various
Adult
4
NA
126
0.9706
0.9449
NA
NA
NA
NA
Survival rate (%)
17 years
NA
NA
NA
NA
NA
NA
NA
NA
8102
5429
NA
The control and hysterectomy data is taken from the placebo of two parrellel studies run by the WHI. Women in both studies were matched for age and various variables, although some differences between the cohorts were present as would be typical.
NA
NA
Homo_sapiens
mortality
146
153
Deleuze et al 2021
No
Tubectomy
No
Intact (no surgery)
No
Female
Macaca fascicularis
Long-tailed Macaques
NA
NA
Yes
Adult (most) and a few subadult
NA
4
NA
127
0.8600
0.8700
NA
NA
NA
NA
Survival rate (%)
3 years
NA
NA
NA
NA
NA
NA
NA
NA
22
39
NA
2017-2018 cohort. Authors sent me additional data for control female animals that had been marked, and were tracked over the same time period from the same population.
NA
NA
Macaca_fascicularis
mortality
147
154
Deleuze et al 2021
No
Tubectomy
No
Intact (no surgery)
No
Female
Macaca fascicularis
Long-tailed Macaques
NA
NA
Yes
Adult (most) and a few subadult
NA
4
NA
128
0.9300
0.9500
NA
NA
NA
NA
Survival rate (%)
Annual
NA
NA
NA
NA
NA
NA
NA
NA
41
43
NA
2018-2019 cohort. Authors sent me additional data for control female animals that had been marked, and were tracked over the same time period from the same population.
NA
NA
Macaca_fascicularis
mortality
148
155
Deleuze et al 2021
No
Tubectomy
No
Intact (no surgery)
No
Female
Macaca fascicularis
Long-tailed Macaques
NA
NA
Yes
Adult (most) and a few subadult
NA
4
NA
129
0.9500
0.9800
NA
NA
NA
NA
Survival rate (%)
Annual
NA
NA
NA
NA
NA
NA
NA
NA
134
45
NA
2019-2020 cohort. Authors sent me additional data for control female animals that had been marked, and were tracked over the same time period from the same population.
NA
NA
Macaca_fascicularis
mortality
149
156
Wang et al 2021
Yes
Castration
Yes
Sham surgery
Yes
Male
Mus musculus
Mice
C57BL6
NA
No
8 months
Adult
4
NA
130
933.5000
938.5000
NA
NA
NA
NA
Median
NA
SD
129.000000
117.000000
NA
NA
NA
NA
NA
22
22
NA
Extended figure 3I
NA
NA
Mus_musculus
longevity
150
157
Wang et al 2021
Yes
Castration
Yes
Sham surgery
Yes
Male
Mus musculus
Mice
NA
NA
No
18 months
Adult
4
NA
131
974.0000
959.0000
NA
NA
NA
NA
Median
NA
SD
95.000000
93.000000
NA
NA
NA
NA
NA
19
19
NA
Fig 6q Animals had been injected with a control shRNA
NA
NA
Mus_musculus
longevity
151
158
Tidiere 2016
No
Various
NA
Intact (no surgery)
No
Female
Varecia rubra
Red ruffed lemur
NA
Zoo
No
Unknown
Unknown
NA
NA
132
15.6500
19.8400
16.180
NA
NA
NA
Mean
NA
SD
12.454792
10.210810
13.436925
NA
NA
NA
NA
689
67
927
Data as described in Fig 7.3 of thesis, from 1 year of age and split for each species
NA
NA
Varecia_rubra
longevity
152
159
Tidiere 2016
No
Various
NA
Intact (no surgery)
No
Male
Varecia rubra
Red ruffed lemur
NA
Zoo
No
Unknown
Unknown
NA
NA
133
16.1800
19.4000
15.650
NA
NA
NA
Mean
NA
SD
13.436925
11.495374
12.454792
NA
NA
NA
NA
927
38
689
Data as described in Fig 7.3 of thesis, from 1 year of age and split for each species
NA
NA
Varecia_rubra
longevity
153
160
Tidiere 2016
No
Various
NA
Intact (no surgery)
No
Female
Varecia variegata
Black and white ruffed lemur
NA
Zoo
No
Unknown
Unknown
NA
NA
134
13.9500
18.0300
13.820
NA
NA
NA
Mean
NA
SD
13.122827
12.489796
14.485185
NA
NA
NA
NA
1542
36
1999
Data as described in Fig 7.3 of thesis, from 1 year of age and split for each species
NA
NA
Varecia_variegata
longevity
154
161
Tidiere 2016
No
Various
NA
Intact (no surgery)
No
Male
Varecia variegata
Black and white ruffed lemur
NA
Zoo
No
Unknown
Unknown
NA
NA
134
13.8200
15.8600
13.950
NA
NA
NA
Mean
NA
SD
14.485185
12.320698
13.122827
NA
NA
NA
NA
1999
37
1542
Data as described in Fig 7.3 of thesis, from 1 year of age and split for each species
NA
NA
Varecia_variegata
longevity
155
162
Larsen1969
Yes
Gonadectomy
Yes
Intact (no surgery)
No
Female
Lamperta fluviatilis
River Lamprey
NA
Lab but wild caught
No
Prior to sexual maturity - Jan
NA
2
NA
135
0.1000
0.3000
NA
NA
NA
NA
Survival rate (%)
May (after spawning)
NA
NA
NA
NA
NA
NA
NA
NA
10
10
10
Data extracted from Figures 6D in Larsen 1973 (thesis) and the published abstract. Have calculated survival rate compared to when there is only one control animal alive so we can calculate a log-response ratio. Data is given as the number of gonadectomized animals that survived past the die-off. From figures 3 and 4 of the thesis there is no gonadectomized animal that dies over April when the last of the control males and females died, so we can be confident that the proportion of alive and dead of control and gonadectomized is correct.
NA
NA
Lamperta_fluviatilis
mortality
156
163
Larsen1969
Yes
Gonadectomy
Yes
Intact (no surgery)
No
Male
Lamperta fluviatilis
River Lamprey
NA
Lab but wild caught
No
Prior to sexual maturity - Jan
NA
2
NA
136
0.1000
0.2700
NA
NA
NA
NA
Survival rate (%)
May (after spawning)
NA
NA
NA
NA
NA
NA
NA
NA
10
11
10
Data extracted from Figures 6D in Larsen 1973 (thesis) and the published abstract. Have calculated survival rate compared to when there is only one control animal alive so we can calculate a log-response ratio. Data is given as the number of gonadectomized animals that survived past the die-off. From figures 3 and 4 of the thesis there is no gonadectomized animal that dies over April when the last of the control males and females died, so we can be confident that the proportion of alive and dead of control and gonadectomized is correct
NA
NA
Lamperta_fluviatilis
mortality
157
164
Larsen 1973
Yes
Gonadectomy
Yes
Intact (no surgery)
No
Female
Lamperta fluviatilis
River Lamprey
NA
Lab but wild caught
No
Prior to sexual maturity - Either Jan or October prior year
NA
2
NA
137
0.0600
0.2500
NA
NA
NA
NA
Survival rate (%)
May (after spawning)
NA
NA
NA
NA
NA
NA
NA
NA
17
20
50
Data extracted from Figures 6E in Lrsden 1973 (thesis). Sample sizes at the start are in Table 11. Have calculated survival rate compared to when there is only one control animal alive so we can calculate a log-response ratio. Data is given as the number of gonadectomized animals that survived past the die-off. From figures 3 and 4 of the thesis there is no gonadectomized animal that dies over April when the last of the control males and females died, so we can be confident that the proportion of alive and dead of control and gonadectomized is correct
NA
NA
Lamperta_fluviatilis
mortality
158
165
Larsen 1973
Yes
Gonadectomy
Yes
Intact (no surgery)
No
Male
Lamperta fluviatilis
River Lamprey
NA
Lab but wild caught
No
Prior to sexual maturity - Either Jan or October prior year
NA
2
NA
138
0.0200
0.2500
NA
NA
NA
NA
Survival rate (%)
May (after spawning)
NA
NA
NA
NA
NA
NA
NA
NA
50
20
17
Data extracted from Figures 6E in Lrsden 1973 (thesis). Sample sizes at the start are in Table 12, pooled for the two treatment times. Sample size for the controls comes from fig 4 where heart weight is given just for the same year cohort and sample.sizes are shown. It is started that they have studied approximately 200 animals over the 4 years and never seen a control live much past spawning but this gives a definitive value for that year. Have calculated survival rate compared to when there is only one control animal alive so we can calculate a log-response ratio. Data is given as the number of gonadectomized animals that survived past the die-off. From figures 3 and 4 of the thesis there is no gonadectomized animal that dies over April when the last of the control males and females died, so we can be confident that the proportion of alive and dead of control and gonadectomized is correct
This data is a subset of the main data only including data from rodents. This is used to test the effect of sterilization/castration on life stage (for these species, we were able to get more fine scale data). This dataset has two more variables: 1) Age_at_treatment (age in days when treatment was applied) and 2) Day_to_maturity (age in days when animals become mature). As also mentioned below, the analyses associated with this data set is post-hoc (not planned before data collection).
Code
#sdat <- read_csv(here("data", "data2_15022022.csv"), na = c("", "NA")) rdat <-read_csv(here("data", "literature","data3_05052022.csv"), na =c("", "NA")) rdat <- rdat %>%filter(is.na(Treatment_lifespan_variable) ==FALSE) %>%#filter(Type_of_sterilization != "Vasectomy") %>% mutate_if(is.character, as.factor) #dim(rdat)# separating two kindseffect_type_r <-ifelse(str_detect(rdat$Lifespan_parameter, "Me"), "longevity", "mortality")# effect-level ID#dat$Species_Latin <- gsub("Macaca Fascicularis", "Macaca fascicularis", dat$Species_Latin) #fix a typo in species namerdat$Effect_ID <-1:nrow(rdat)rdat$Phylogeny <-sub(" ", "_", rdat$Species_Latin)rdat$Effect_type <- effect_type_r# key variables#names(rdat)#unique(rdat$Species_Latin)kable(rdat, "html") %>%kable_styling("striped", position ="left") %>%scroll_box(width ="100%", height ="250px")
Order_extracted
Study
Controlled_treatments
Type_of_sterilization
Gonads_removed
Control_treatment
Sex
Day_to_matuarity
Species_Latin
Species
Strain
Environment
Wild_or_semi_wild
Age_at_treatment
Age_at_treatment_continuous
Maturity_at_treatment
Maturity_at_treatment_ordinal
Duration_of_treatment
Shared_control
Control_lifespan_variable
Treatment_lifespan_variable
Opposite_sex_lifespan_variable
Error_control
Error_experimental
Error_opposite_sex
Lifespan_parameter
Lifespan_unit
Error_unit
Error_control_SD
Error_experimental_SD
Error_opposite_sex_SD
Coefficent_difference_to_control
Lower_interval
Upper_interval
Coefficent_unit
Sample_size_control
Sample_size_sterilization
Sample_size_opposite_sex
Notes
Effect_ID
Phylogeny
Effect_type
13
Zakeri et al 2019
Yes
Ovariectomy
Yes
Intact (no surgery)
Female
42
Mus musculus
Mice
NMRI
Laboratory
No
10 months
304.0
Adult (old)
4
NA
11
0.360
0.500
NA
NA
NA
NA
Survival rate (%)
11.5 months
NA
NA
NA
NA
NA
NA
NA
NA
16
16
NA
Its the sterilization treatment that is compared to two different types of control in this study
1
Mus_musculus
mortality
14
Zakeri et al 2019
Yes
Ovariectomy
Yes
Sham surgery
Female
42
Mus musculus
Mice
NMRI
Laboratory
No
10 months
304.0
Adult (old)
4
NA
11
0.330
0.500
NA
NA
NA
NA
Survival rate (%)
11.5 months
NA
NA
NA
NA
NA
NA
NA
NA
16
16
NA
Its the sterilization treatment that is compared to two different types of control in this study
2
Mus_musculus
mortality
15
Dorner 1973
Yes
Castration
Yes
Untreated
Male
70
Rattus norvegicus
Rats
Sprague-Dawley-Stammes
Laboratory
No
Day after birth
2.0
Birth
1
NA
12
570.000
696.000
NA
122.00
132.00
NA
Mean
days
Standard Deviation
122.00000
132.00000
NA
NA
NA
NA
NA
12
8
NA
NA
3
Rattus_norvegicus
longevity
16
Asdell et al 1967
Yes
Ovariectomy
Yes
Intact (no surgery)
Female
90
Rattus norvegicus
Rats
Cornell Nutrion colony
Laboratory
No
Between 38-42 days
40.0
Puberty
3
NA
13
742.000
669.000
615
24.00
26.00
21.00
Mean
days
S.E.M
169.70563
183.84776
148.49242
NA
NA
NA
NA
50
50
50
Also data for mated females but havent included as would be a different environment (e.g. With males)
4
Rattus_norvegicus
longevity
17
Asdell et al 1967
Yes
Castration
Yes
Intact (no surgery)
Male
70
Rattus norvegicus
Rats
Cornell Nutrion colony
Laboratory
No
Between 39-42 days
41.0
Puberty
3
NA
14
615.000
651.000
742
21.00
26.00
24.00
Mean
days
S.E.M
148.49242
183.84776
169.70563
NA
NA
NA
NA
50
50
50
NA
5
Rattus_norvegicus
longevity
18
Asdell and Joshi 1976
Yes
Ovariectomy
Yes
Intact (no surgery)
Female
90
Rattus norvegicus
Rats
Manor-Wistar
Laboratory
No
45 days old
45.0
Puberty
3
NA
15
654.000
844.000
661
24.00
24.00
30.00
Mean
days
S.E.M
169.70563
169.70563
212.13203
NA
NA
NA
NA
50
50
50
NA
6
Rattus_norvegicus
longevity
19
Asdell and Joshi 1976
Yes
Castration
Yes
Intact (no surgery)
Male
70
Rattus norvegicus
Rats
Manor-Wistar
Laboratory
No
45 days old
45.0
Puberty
3
NA
16
661.000
775.000
654
30.00
30.00
24.00
Mean
days
S.E.M
212.13203
212.13203
169.70563
NA
NA
NA
NA
50
50
50
NA
7
Rattus_norvegicus
longevity
20
Arriola Apelo et al 2020
Yes
Castration
Yes
Sham surgery
Male
42
Mus musculus
Mice
C57BL6
Laboratory
No
21 days
21.0
Prepuberty
2
NA
17
1006.000
978.000
853
34.30
37.45
26.25
Median
days
S.E.M
145.52258
183.46678
136.39900
NA
NA
NA
NA
18
24
27
NA
8
Mus_musculus
longevity
21
Arriola Apelo et al 2020
Yes
Ovariectomy
Yes
Sham surgery
Female
42
Mus musculus
Mice
C57BL6
Laboratory
No
21 days
21.0
Prepuberty
2
NA
18
853.000
916.000
1006
26.25
49.36
34.30
Median
days
S.E.M
136.39900
231.51892
145.52258
NA
NA
NA
NA
27
22
18
NA
9
Mus_musculus
longevity
22
Benedusi et al 2015
Yes
Castration
Yes
Sham surgery
Male
42
Mus musculus
Mice
C57BL76 (ERE-LucRepTOP™)
Laboratory
No
5 Months
152.0
Adult (old)
4
NA
19
0.100
0.350
NA
NA
NA
NA
Survival rate (%)
15 Months
NA
NA
NA
NA
NA
NA
NA
NA
20
20
NA
NA
10
Mus_musculus
mortality
24
Cargil et al 2003
Yes
Ovariectomy
Yes
Intact (no surgery)
Female
42
Mus musculus
Mice
CBA
Laboratory
No
21 days
21.0
Prepuberty
2
NA
21
599.290
578.640
NA
30.45
35.60
NA
Median
Days
S.E.M
158.22284
178.00000
NA
NA
NA
NA
NA
27
25
NA
Extracted median lifespan from data in figure and calculated SD
11
Mus_musculus
longevity
31
Drori and Folman 1976
Yes
Castration
Yes
Intact (no surgery)
Male
42
Rattus norvegicus
Rats
Albino
Laboratory
No
38-44 days. Stated as prepuberty
41.0
Prepuberty
2
NA
27
727.000
817.000
849
26.00
32.00
26.00
Mean
Days
S.E.M
182.00000
224.00000
182.00000
NA
NA
NA
NA
49
49
49
Authors state that they castrated animals shortly before puberty, so coded as prepuberty
12
Rattus_norvegicus
longevity
32
Garratt et al 2021
Yes
Castration
Yes
Sham surgery
Male
70
Mus musculus
Mice
C57BL6
Laboratory
No
7-8 weeks
53.0
Adult (young)
4
NA
28
952.000
960.000
956
20.70
36.40
28.50
Median
Days
S.E.M
117.09688
218.40000
156.10093
NA
NA
NA
NA
32
36
30
NA
13
Mus_musculus
longevity
42
Holland et al 1977
Yes
Ovariectomy
Yes
Intact (no surgery)
Female
42
Mus musculus
Mice
RFM
Laboratory
No
3-4 weeks
25.0
Prepuberty
2
NA
34
638.000
628.000
NA
16.00
16.00
NA
Mean
Days
S.E.M
167.04490
162.38226
NA
NA
NA
NA
NA
109
103
NA
Just used data from non-irradiated group. Lots of pathology data
14
Mus_musculus
longevity
45
Sichuk 1965
Yes
Castration
Yes
Sham surgery
Male
48
Mesocricetus auratus
Hampsters
Syrian Hampsters
Laboratory
No
6 weeks
42.0
Puberty
3
NA
37
612.000
578.000
589
NA
NA
NA
Mean
Days
NA
222.91000
151.30000
222.95000
NA
NA
NA
NA
92
90
94
Use - SD from Kirkman & Yau;Error not provided. Mean lifespan comes from the lifespan of both those with thrumobis and those that do not have it. Calculated the mean of these two values weighed against the sample size of each group
15
Mesocricetus_auratus
longevity
46
Sichuk 1965
Yes
Ovariectomy
Yes
Sham surgery
Female
48
Mesocricetus auratus
Hampsters
Syrian Hampsters
Laboratory
No
6 weeks
42.0
Puberty
3
NA
38
589.000
586.000
612
NA
NA
NA
Mean
Days
NA
222.95000
155.44000
222.91000
NA
NA
NA
NA
94
92
92
Use - SD from Kirkman & Yau;Error not provided. Mean lifespan comes from the lifespan of both those with thrumobis and those that do not have it. Calculated the mean of these two values weighed against the sample size of each group
16
Mesocricetus_auratus
longevity
51
Slonaker 1930
Yes
Castration
Yes
Intact (no surgery)
Male
70
Rattus norvegicus
Rats
Albino
Laboratory
No
44 days. Testes had decended by the operation
44.0
Adult (young)
4
NA
43
788.000
770.000
863
22.25
28.00
27.69
Mean
Days
Probable error
32.98740
41.51223
41.05263
NA
NA
NA
NA
10
8
17
NA
17
Rattus_norvegicus
longevity
52
Slonaker 1930
Yes
Vasectomy
No
Intact (no surgery)
Male
70
Rattus norvegicus
Rats
Albino
Laboratory
No
46.5 days. Testes had decended by the operation
47.0
Adult (young)
4
NA
43
788.000
685.000
863
22.25
39.72
27.69
Mean
Days
Probable error
32.98740
58.88807
41.05263
NA
NA
NA
NA
10
12
17
Standard deviation calculated from probable error as P.E./0.6745. Calculation derived from P.E. Wikipedia page
18
Rattus_norvegicus
longevity
53
Slonaker 1930
Yes
Ovariectomy
Yes
Intact (no surgery)
Female
90
Rattus norvegicus
Rats
Albino
Laboratory
No
27.5 days
28.0
Prepuberty
2
NA
44
863.000
755.000
788
27.69
22.15
22.25
Mean
Days
Probable error
41.05263
32.83914
32.98740
NA
NA
NA
NA
17
37
10
NA
19
Rattus_norvegicus
longevity
54
Slonaker 1930
Yes
Hysterectomy
No
Intact (no surgery)
Female
90
Rattus norvegicus
Rats
Albino
Laboratory
No
29 days
29.0
Prepuberty
2
NA
44
863.000
855.000
788
27.69
12.67
22.25
Mean
Days
Probable error
41.05263
18.78428
32.98740
NA
NA
NA
NA
17
60
10
NA
20
Rattus_norvegicus
longevity
55
Storer et al 1982
Yes
Ovariectomy
Yes
Intact (no surgery)
Female
42
Mus musculus
Mice
RFM
Laboratory
No
50 days
50.0
Adult (young)
4
NA
45
643.400
662.200
NA
5.91
7.31
NA
Mean
Days
S.E.M.
161.31161
134.19376
NA
NA
NA
NA
NA
745
337
NA
Non-irradiated controls from an irradiation experiment
21
Mus_musculus
longevity
56
Storer et al 1982
Yes
Ovariectomy
Yes
Intact (no surgery)
Female
42
Mus musculus
Mice
Balb/c
Laboratory
No
50 days
50.0
Adult (young)
4
NA
46
762.900
795.500
NA
6.21
10.95
NA
Mean
Days
S.E.M.
179.01611
197.70740
NA
NA
NA
NA
NA
831
326
NA
Non-irradiated controls from an irradiation experiment
22
Mus_musculus
longevity
61
Mason et al 2009
Yes
Ovariectomy
Yes
Intact (no surgery)
Female
42
Mus musculus
Mice
CBA
Laboratory
No
21 days
21.0
Prepuberty
2
NA
51
727.600
715.000
NA
15.90
20.00
NA
Mean
Days
S.E.M
89.94398
101.98039
NA
NA
NA
NA
NA
32
26
NA
Worked out sample size from fig 4 This and the other entry for this paper have two different control comparisons
23
Mus_musculus
longevity
62
Mason et al 2009
Yes
Ovariectomy
Yes
Intact (no surgery)
Female
42
Mus musculus
Mice
CBA
Laboratory
No
21 days
21.0
Prepuberty
2
NA
51
725.600
715.000
NA
20.40
20.00
NA
Mean
Days
S.E.M
117.18908
101.98039
NA
NA
NA
NA
NA
33
26
NA
Worked out sample size from fig 4
24
Mus_musculus
longevity
63
Talbert and Hamilton 1965
Yes
Castration
Yes
Sham surgery
Male
70
Rattus norvegicus
Rats
Lewis
Laboratory
No
Birth
1.0
Birth
1
NA
52
454.000
521.000
484
18.00
27.00
19.00
Mean
Days
S.E.M
108.00000
174.98000
123.13407
NA
NA
NA
NA
36
42
42
NA
25
Rattus_norvegicus
longevity
64
Talbert and Hamilton 1965
Yes
Castration
Yes
Sham surgery
Male
70
Rattus norvegicus
Rats
Lewis
Laboratory
No
22-28 days
25.0
Prepuberty
2
NA
52
454.000
488.000
484
18.00
28.00
19.00
Mean
Days
S.E.M
108.00000
165.65023
123.13407
NA
NA
NA
NA
36
35
42
NA
26
Rattus_norvegicus
longevity
65
Talbert and Hamilton 1965
Yes
Castration
Yes
Sham surgery
Male
70
Rattus norvegicus
Rats
Lewis
Laboratory
No
100 days
100.0
Adult (young)
4
NA
52
454.000
439.000
484
18.00
25.00
19.00
Mean
Days
S.E.M
108.00000
119.89579
123.13407
NA
NA
NA
NA
36
23
42
NA
27
Rattus_norvegicus
longevity
66
Talbert and Hamilton 1965
Yes
Ovariectomy
Yes
Sham surgery
Female
90
Rattus norvegicus
Rats
Lewis
Laboratory
No
Birth
1.0
Birth
1
NA
53
484.000
574.000
454
19.00
33.00
18.00
Mean
Days
S.E.M
123.13407
183.73622
108.00000
NA
NA
NA
NA
42
31
36
NA
28
Rattus_norvegicus
longevity
67
Talbert and Hamilton 1965
Yes
Ovariectomy
Yes
Sham surgery
Female
90
Rattus norvegicus
Rats
Lewis
Laboratory
No
22-28 days
25.0
Prepuberty
2
NA
53
484.000
480.000
454
19.00
44.00
18.00
Mean
Days
S.E.M
123.13407
206.37829
108.00000
NA
NA
NA
NA
42
22
36
NA
29
Rattus_norvegicus
longevity
68
Talbert and Hamilton 1965
Yes
Ovariectomy
Yes
Sham surgery
Female
90
Rattus norvegicus
Rats
Lewis
Laboratory
No
100 days
100.0
Adult (young)
4
NA
53
484.000
515.000
454
19.00
41.00
18.00
Mean
Days
S.E.M
123.13407
183.35757
108.00000
NA
NA
NA
NA
42
20
36
NA
30
Rattus_norvegicus
longevity
94
Muhlock 1959
Yes
Castration
Yes
Intact (no surgery)
Male
42
Mus musculus
Mice
DBA
Laboratory
No
Weaning (1month)
30.0
Prepuberty
2
NA
75
578.000
595.000
667
10.12
11.44
9.57
Mean
days
S.E.M
78.38918
102.32247
88.74853
NA
NA
NA
NA
60
80
86
Extracted data and calculated mean and SE from graph
31
Mus_musculus
longevity
95
Muhlock 1959
Yes
Ovariectomy
Yes
Intact (no surgery)
Female
42
Mus musculus
Mice
DBA
Laboratory
No
Weaning (1 month)
30.0
Prepuberty
2
NA
76
667.000
627.000
578
9.57
10.67
10.12
Mean
days
S.E.M
88.74853
87.98707
78.38918
NA
NA
NA
NA
86
68
60
Extracted data and calculated mean and SE from graph
32
Mus_musculus
longevity
99
Iwasa et al 2018
Yes
Ovariectomy
Yes
Sham surgery
Female
90
Rattus norvegicus
Rats
Sprague-Dawley
Laboratory
No
23 weeks
161.0
Late adult
4
NA
80
0.430
0.860
NA
NA
NA
NA
Survival rate (%)
~85 weeks.
NA
NA
NA
NA
NA
NA
NA
NA
8
7
NA
Calculated from a partial survival curve. Looked at when 50% of the control group died and then the number alive in the treatment group at this point
33
Rattus_norvegicus
mortality
114
Cheng 2019
Yes
Castration
Yes
Sham surgery
Male
42
Mus musculus
Mice
UMHet3
Laboratory
No
Under 30 days
17.5
Prepuberty
2
NA
91
0.810
0.970
NA
NA
NA
NA
Survival rate (%)
500 days
NA
NA
NA
NA
NA
NA
NA
NA
238
238
NA
NA
34
Mus_musculus
mortality
132
Sato et al 1997
Yes
Ovariectomy
Yes
Sham surgery
Female
90
Rattus norvegicus
Rats
Sprague-Dawley
Laboratory
No
9 months
274.0
Adult
4
NA
109
0.800
0.971
NA
NA
NA
NA
Survival rate (%)
6 months
NA
NA
NA
NA
NA
NA
NA
NA
35
35
NA
NA
35
Rattus_norvegicus
mortality
135
Pullinger and Head 1964
Yes
Ovariectomy
Yes
untreated
Female
42
Mus musculus
Mice
C3Hf
Laboratory
No
56-111 days of age
84.0
Adult
4
NA
112
0.370
0.230
NA
NA
NA
NA
Survival rate (%)
to 30 months
NA
NA
NA
NA
NA
NA
NA
NA
114
40
NA
"average lifespan" in months is also provided but no error. Calculated the percentage surviving to 30 months as dead ranges are provided in 6 brackets and this is the closest to the median for control females. This group is control virgin females (OVX data was shared and compared to breeding females in the other female comparison for this paper but I removed it because I dont think social environment was comparable)
36
Mus_musculus
mortality
145
Hotchkiss 1995
Yes
Ovariectomy
Yes
Sham surgery
Female
90
Rattus norvegicus
Rats
Sprague-Dawley
Laboratory
No
90 days
90.0
Adult
4
NA
120
0.333
0.833
NA
NA
NA
NA
Survival rate (%)
to 630 days
NA
NA
NA
NA
NA
NA
NA
NA
12
12
NA
Survival to 630 days. Animals that presented with subquaneous tumors had these surgically removed.
37
Rattus_norvegicus
mortality
151
Phelan 1995
Yes
Ovariectomy
Yes
Sham surgery
Female
42
Mus musculus
Mice
Swiss
Laboratory
No
Weaning
25.0
Weaning
2
NA
125
0.410
0.620
NA
NA
NA
NA
Survival rate (%)
To 800 days
NA
NA
NA
NA
NA
NA
NA
NA
30
30
NA
Animals were maintained on a 90% of adlibitum diet. This was a control group for a sepeate CR study and they state stopped the animals getting fat.
38
Mus_musculus
mortality
156
Wang et al 2021
Yes
Castration
Yes
Sham surgery
Male
42
Mus musculus
Mice
C57BL6
NA
No
8 months
243.0
Adult
4
NA
130
933.500
938.500
NA
NA
NA
NA
Median
NA
SD
129.00000
117.00000
NA
NA
NA
NA
NA
22
22
NA
Extended figure 3I
39
Mus_musculus
longevity
157
Wang et al 2021
Yes
Castration
Yes
Sham surgery
Male
42
Mus musculus
Mice
NA
NA
No
18 months
548.0
Adult
4
NA
131
974.000
959.000
NA
NA
NA
NA
Median
NA
SD
95.00000
93.00000
NA
NA
NA
NA
NA
19
19
NA
Fig 6q Animals had been injected with a control shRNA
This dataset is a part of the full data and only has data from rodent species.
This data sets is a subset of the main data and this includes only study which has all 4 groups: 1) control females, 2) control males, 3) treated females and 4) treated males.
Lifespan of individuals calculated from histograms. Used data from animals that survived after the first year.
4
Felis_catus
longevity
117
Bronson 1982
No
No
Castration
Yes
Intact (no surgery)
Canis lupus
Dogs
Various breeds
Domestic
Various
94
8.00
5.200000
755
9.90
6.800000
54
7.70
4.400000
224
8.80
6.900000
528
Mean (from those alive after 2)
Years
Mean lifespan is that of those that are alive from 2 years of age. The authors also provide lifespan from birth, but point out that animals are not usually sterilized until at least 6 months, so this doesnt provide a fair comparison
5
Canis_lupus
longevity
72
Hamilton 1965
No
No
Castration
Yes
Intact (no surgery)
Felis catus
Cats
Various breeds
Domestic
Before 1 year
57
3.20
2.741168
65
6.80
4.492661
60
7.70
5.178726
58
9.20
4.656522
28
Mean
Years
Error displayed must be standard error not standard deviation. I initially interpreted the symbol used as standard deviation but actually it must be SEM, and this is what Hamilton usually uses. Otherwise they are abnormally low.
6
Felis_catus
longevity
74
Hamilton 1965
No
No
Castration
Yes
Intact (no surgery)
Felis catus
Cats
Various breeds
Domestic
Before 1 year
59
6.10
4.713343
51
8.50
4.913980
77
7.40
5.091169
50
8.40
4.762825
45
Mean
Years
Error displayed must be standard error not standard deviation. I initially interpreted the symbol used as standard deviation but actually it must be SEM, and this is what Hamilton usually uses. Otherwise they are abnormally low.
7
Felis_catus
longevity
33
Hamilton et al 1969
No
No
Castration
Yes
Intact (no surgery)
Felis catus
Cats
Outbred
Domestic
Various
29
5.30
4.136520
97
8.10
4.820332
201
7.70
4.794163
85
8.20
4.503332
75
Mean
Years
Pooled data for all ages which is consistent for both castrated and spayed of both sexes.
8
Felis_catus
longevity
38
Hamilton et al 1969
No
No
Castration
Yes
Intact (no surgery)
Felis catus
Cats
Name breeds
Domestic
Various, median 6 months
32
4.60
3.500000
25
6.90
4.971428
71
6.20
5.040000
36
8.20
4.723071
34
Mean
Years
NA
9
Felis_catus
longevity
57
Hoffman et al 2018
No
No
Castration
Yes
Intact (no surgery)
Canis lupus
Dogs
Various breeds
Domestic
Various
47
10.86
3.327386
915
11.64
2.033618
844
10.86
3.685485
693
12.12
5.766116
921
Mean
Years
Vetcompass database
10
Canis_lupus
longevity
59
Hoffman et al 2018
No
No
Castration
Yes
Intact (no surgery)
Canis lupus
Dogs
Various breeds
Domestic
Various
49
8.00
8.556337
14941
9.21
4.241509
11244
7.68
6.018372
7392
9.73
5.599714
19598
Mean
Years
VMDB - individual data for breeds available in supplementary, but just mean lifespan without error
11
Canis_lupus
longevity
76
Huang et al 2017
No
No
Castration
Yes
Intact (no surgery)
Canis lupus
Dogs
Various breeds
Domestic
Various
61
9.00
5.941000
839
12.00
3.723000
332
10.00
5.947000
528
12.00
3.938000
607
Median
Years
Interquartile range Intact, 5.0-13.0; castrated 9.0-14.0
12
Canis_lupus
longevity
43
Kirkman and Yau
No
No
Castration
Yes
Intact (no surgery)
Mesocricetus auratus
Hampsters
Syrian Hampsters
Laboratory
Unknown - not given
35
632.00
222.910000
629
508.00
151.300000
72
543.00
222.950000
578
391.00
155.440000
31
Mean
Days
Do not have error presented in paper. The proportion of animals dying in 10 brackets of different ages is presented that could be used (Figs 3 and 4). Upper and low quartiles estimated from figure 3 when number of animals dying in 100 day periods is given. Have used the middle number within this period for the quartile value (e.g. 550-850 for intact males, 350-550 for castrated males)
13
Mesocricetus_auratus
longevity
47
Mitchel et al 1999
No
No
Castration
Yes
Intact (no surgery)
Canis lupus
Dogs
Various breeds
Domestic
Various
39
131.00
50.029192
1277
128.00
46.058550
291
130.00
51.951131
833
144.00
40.249224
720
Mean
Months
Used all causes of death. Data collected on the basis of surveys that pet owners filled out about their last pets age at death
14
Canis_lupus
longevity
94
Muhlock 1959
Yes
No
Castration
Yes
Intact (no surgery)
Mus musculus
Mouse
DBA
Laboratory
Weaning
75
578.00
78.389183
60
595.00
102.322471
80
667.00
88.748529
86
627.00
87.987074
68
Mean
days
Extracted data and calculated mean and SE from graph
15
Mus_musculus
longevity
105
Oneil et al 2013
No
No
Castration
Yes
Intact (no surgery)
Canis lupus
Dogs
Various breeds
Domestic
Various
83
11.99
NA
1464
11.99
NA
1224
11.59
NA
1082
12.39
NA
1304
Mean
Years
Means calculated from the coefficient change in lifespan in Table 4. Sample size for all was known, SD was estimated
16
Canis_lupus
longevity
107
Oneil et al 2015
No
No
Castration
Yes
Intact (no surgery)
Felis catus
Cats
Various breeds
Domestic
Various
85
12.81
NA
704
14.71
NA
1296
14.61
NA
707
15.21
NA
1302
Mean
Years
Means calculated from the coefficient change in lifespan in Table 4. Sample size for each sex was estimated on the basis of the total sample size and the percentage that were known to be sterilized in both sexes, SD was estimated
17
Felis_catus
longevity
30
Reedy et al 2016
Yes
Yes
Castration
Yes
Sham surgery
Anolis sagrei
Anole lizards
NA
Wild
Unknown - wild caught
26
0.55
NA
60
0.28
NA
60
0.25
NA
110
0.21
NA
110
Survival rate (%)
10 weeks (of breeding season)
NA
18
Anolis_sagrei
mortality
45
Sichuk 1965
Yes
No
Castration
Yes
Sham surgery
Mesocricetus auratus
Hampsters
Syrian Hampsters
Laboratory
6 weeks
37
612.00
222.910000
92
578.00
151.300000
90
589.00
222.950000
94
586.00
155.440000
92
Mean
Days
Use - SD from Kirkman & Yau;Error not provided. Mean lifespan comes from the lifespan of both those with thrumobis and those that do not have it. Calculated the mean of these two values weighed against the sample size of each group
Data as described in Fig 7.3 of thesis, from 1 year of age and split for each species
26
Varecia_rubra
longevity
161
Tidiere 2016
No
No
Unknown
Unknown
Intact (no surgery)
Varecia variegata
Black and white ruffed lemur
NA
Zoo
Unknown
134
13.82
14.485185
1999
15.86
12.320698
37
13.95
13.122827
1542
18.03
12.489796
36
Mean
Years
Data as described in Fig 7.3 of thesis, from 1 year of age and split for each species
27
Varecia_variegata
longevity
129
Kent et al 2018
No
No
Castration
Yes
Intact (no surgery)
Canis lupus
Dogs
Golden retriver
Domestic
Unknown
106
8.68
3.230000
118
9.35
2.710000
228
5.89
3.270000
58
9.51
2.740000
248
Median
Years
Standard deviation calculated from the range according to Wan et al 2014
28
Canis_lupus
longevity
136
Robertson et al 1961
No
No
Castration
Yes
untreated
Oncorhynchus nerka
Kokanee salmon
NA
Experimental pond
2 years 1 month
113
4.05
0.590000
41
5.31
1.600000
13
4.26
0.550000
58
5.89
1.630000
16
Mean
Years
Only used data on animals that were found dead and did not have regenerating gonads. Mortality data is presented for a larger cohort that were also exposed to predation but control data is not seperated according to sex.
29
Oncorhynchus_nerka
longevity
141
Urfer et al 2020
No
No
Castration
Yes
Intact (no surgery)
Canis lupus
Dogs
NA
Various
Various
117
15.00
17.598000
2115
15.20
16.530000
8567
14.10
20.090000
1551
15.80
16.670000
8711
Median survival time
Years
Calculated SD looks high? Another MSL from age 5 years is provided by not sure of the sample size included
30
Canis_lupus
longevity
162
Larsen1969
Yes
No
Gonadectomy
Yes
Intact (no surgery)
Lamperta fluviatilis
River Lamprey
NA
Lab but wild caught
Prior to sexual maturity - Jan
135
0.10
NA
10
0.27
NA
10
0.10
NA
10
0.30
NA
10
Survival rate (%)
May (after spawning)
Data extracted from Figures 6D in Larsen 1973 (thesis) and the published abstract
31
Lamperta_fluviatilis
mortality
164
Larsen 1973
Yes
No
Gonadectomy
Yes
Intact (no surgery)
Lamperta fluviatilis
River Lamprey
NA
Lab but wild caught
Prior to sexual maturity - Either Jan or October prior year
137
0.02
NA
50
0.25
NA
20
0.06
NA
17
0.25
NA
20
Survival rate (%)
May (after spawning)
Data extracted from Figures 6E in Lrsden 1973 (thesis). Sample sizes at the start are in Table 11
32
Lamperta_fluviatilis
mortality
Code
# we create a longer data formatsdat1 <- sdatsdat2 <- sdat# lnRR# here we create the ratio of M/Fsdat1$yi <-ifelse(effect_type_s =="longevity", lnrrm(sdat$Male_control_lifespan_variable, sdat$Female_control_lifespan_variable, sdat$Sample_size_male_control, sdat$Sample_size_female_control, cvs[["cv2_cont"]],cvs[["cv2_cont"]])[[1]], lnrrp(sdat$Male_control_lifespan_variable, sdat$Female_control_lifespan_variable, sdat$Sample_size_male_control, sdat$Sample_size_female_control)[[1]])sdat1$vi <-ifelse(effect_type_s =="longevity", lnrrm(sdat$Male_control_lifespan_variable, sdat$Female_control_lifespan_variable, sdat$Sample_size_male_control, sdat$Sample_size_female_control, cvs[["cv2_cont"]],cvs[["cv2_cont"]])[[2]], lnrrp(sdat$Male_control_lifespan_variable, sdat$Female_control_lifespan_variable, sdat$Sample_size_male_control, sdat$Sample_size_female_control)[[2]])# here we create CM/CFsdat2$yi <-ifelse(effect_type_s =="longevity", lnrrm(sdat$Male_sterilization_lifespan_variable, sdat$Female_sterilization_lifespan_variable, sdat$Sample_size_male_sterilization, sdat$Sample_size_female_sterilization, cvs[["cv2_trt"]],cvs[["cv2_trt"]])[[1]], lnrrp(sdat$Male_sterilization_lifespan_variable, sdat$Female_sterilization_lifespan_variable, sdat$Sample_size_male_sterilization, sdat$Sample_size_female_sterilization)[[1]])sdat2$vi <-ifelse(effect_type_s =="longevity", lnrrm(sdat$Male_sterilization_lifespan_variable, sdat$Female_sterilization_lifespan_variable, sdat$Sample_size_male_sterilization, sdat$Sample_size_female_sterilization, cvs[["cv2_trt"]],cvs[["cv2_trt"]])[[2]], lnrrp(sdat$Male_sterilization_lifespan_variable, sdat$Female_sterilization_lifespan_variable, sdat$Sample_size_male_sterilization, sdat$Sample_size_female_sterilization)[[2]])# merging sdata framessdat_long <-rbind(sdat1, sdat2)# putt 2 new columnsdat_long$Obs <-factor(1:dim(sdat_long)[[1]])sdat_long$Comp_type <-as.factor(rep(c("both_normal", "both_castrated"), each =dim(sdat_long)[[1]]/2))
#names(dat)myspecies <-as.character(unique(dat$Species_Latin)) #get list of species#str_sort(myspecies) #visual check#length(myspecies) #23 species#length(unique(myspecies)) #23 unique species names
Using rotl package to retrieve synthetic species tree from Open Tree of Life: Rotl is an R package (https://peerj.com/preprints/1471/) allowing access to synthetic phylogenetic tree available at Open Tree of Life database (https://opentreeoflife.org/).
Code
taxa <-tnrs_match_names(names = myspecies)dim(taxa) #40 specias - all matchedtable(taxa$approximate_match) #1 approximate matchtaxa[taxa$approximate_match ==TRUE, ] ##lamperta fluviatilis (search_string) will be presented as Perca fluviatilis (uniquw_name)
#check overlap and differences with the taxa listintersect(gsub("_"," ", tree$tip.label), myspecies) #22setdiff(gsub("_"," ", tree$tip.label), myspecies) # "Perca fluviatilis" setdiff(myspecies, gsub("_"," ", tree$tip.label)) # "Lamperta fluviatilis"tree$tip.label <-gsub("Perca_fluviatilis", "Lamperta_fluviatilis", tree$tip.label) #replace with the original name#tree <- drop.tip(tree, "Equus_caballus")#re-check overlap and differences with myspecies list#intersect(myspecies, tree2$tip.label) #23#setdiff(myspecies, tree2$tip.label) #0#setdiff(tree2$tip.label, myspecies) #0#check if the tree is really binary is.binary.tree(tree) #TRUE# tree_binary$node.label <- NULL #you can delete internal node labels# *NOTE:* no branch lengths are included, they can be created later via simulations. write.tree(tree, file=here("data", "tree_rotl.tre")) #save the tree# *NOTE:* underscores within species names on tree tip labals are added automatically# tree <- read.tree(file="plot_cooked_fish_MA.tre") #if you need to read in the tree# tree$tip.label <- gsub("_"," ", tree$tip.label) #get rid of the underscores# tree$node.label <- NULL #you can delete internal node labels
Code
tree <-read.tree(here("data", "literature","tree_rotl.tre"))plot(tree, cex=.6, label.offset =.1, no.margin =TRUE)
# VCV matrix to model shared control V_matrix <-impute_covariance_matrix(vi = dat$vi, cluster = dat$Shared_control, r =0.5)# phylogeny to model#tree <- read.tree(here("data/tree_rotl.tre"))tree <-compute.brlen(tree)cor_tree <-vcv(tree, corr =TRUE)# checking the match#match(unique(dat$Phylogeny), colnames(cor_tree))# meta-analysis basics# phylogenetic modelmod <-rma.mv(yi, V = V_matrix, mod =~1, random =list(~1|Phylogeny, ~1|Species_Latin, ~1|Study, ~1|Effect_ID), R =list(Phylogeny = cor_tree), data = dat, test ="t",sparse =TRUE,control=list(optimizer="optim", optmethod="Nelder-Mead") )summary(mod)
Multivariate Meta-Analysis Model (k = 159; method: REML)
logLik Deviance AIC BIC AICc
16.5192 -33.0384 -23.0384 -7.7254 -22.6436
Variance Components:
estim sqrt nlvls fixed factor R
sigma^2.1 0.0011 0.0325 22 no Phylogeny yes
sigma^2.2 0.0166 0.1289 22 no Species_Latin no
sigma^2.3 0.0133 0.1152 71 no Study no
sigma^2.4 0.0080 0.0894 159 no Effect_ID no
Test for Heterogeneity:
Q(df = 158) = 1366.5635, p-val < .0001
Model Results:
estimate se tval df pval ci.lb ci.ub
0.1636 0.0428 3.8232 158 0.0002 0.0791 0.2481 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Code
round(i2_ml(mod),2) # almost no phylogenetic effect
# visualizing the resultorchard_plot(mod, xlab ="log response ratio (lnRR)", group ="Study")
Code
# reduced model without phylogeny # alternativemod2 <-rma.mv(yi, V = V_matrix, mod =~1, random =list(#~1|Phylogeny, ~1|Species_Latin, ~1|Study, ~1|Effect_ID), #R = list(Phylogeny = cor_tree), data = dat, test ="t",sparse =TRUE,control=list(optimizer="optim", optmethod="Nelder-Mead") )summary(mod2)
Multivariate Meta-Analysis Model (k = 159; method: REML)
logLik Deviance AIC BIC AICc
16.5131 -33.0262 -25.0262 -12.7758 -24.7648
Variance Components:
estim sqrt nlvls fixed factor
sigma^2.1 0.0172 0.1310 22 no Species_Latin
sigma^2.2 0.0132 0.1149 71 no Study
sigma^2.3 0.0080 0.0894 159 no Effect_ID
Test for Heterogeneity:
Q(df = 158) = 1366.5635, p-val < .0001
Model Results:
estimate se tval df pval ci.lb ci.ub
0.1589 0.0383 4.1529 158 <.0001 0.0833 0.2345 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Code
# we will not use robust for the analysis - they do not seem to change the results# rob2.2 <- robust(mod2, cluster = Study, adjust=TRUE, clubSandwich=TRUE, verbose=TRUE)# rob2.2anova(mod, mod2) # they are not significantly different
# using alternative effect size: -1/ln(p)V_matrix2 <-impute_covariance_matrix(vi = dat$v2, cluster = dat$Shared_control, r =0.5)#mod_alt <-rma.mv(y2, V = V_matrix2, mod =~1, random =list(~1|Species_Latin, ~1|Study, ~1|Effect_ID), data = dat, test ="t",sparse =TRUE,control=list(optimizer="optim", optmethod="Nelder-Mead") )summary(mod_alt)
Multivariate Meta-Analysis Model (k = 159; method: REML)
logLik Deviance AIC BIC AICc
-72.7948 145.5896 153.5896 165.8400 153.8511
Variance Components:
estim sqrt nlvls fixed factor
sigma^2.1 0.0394 0.1985 22 no Species_Latin
sigma^2.2 0.0137 0.1168 71 no Study
sigma^2.3 0.0025 0.0502 159 no Effect_ID
Test for Heterogeneity:
Q(df = 158) = 676.1277, p-val < .0001
Model Results:
estimate se tval df pval ci.lb ci.ub
0.2384 0.0602 3.9625 158 0.0001 0.1196 0.3573 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Code
round(i2_ml(mod_alt),2) # almost no phylogenetic effect
# no phylogenymod_func <-function(formula) {rma.mv(yi, V = V_matrix,mod = formula, random =list(#~1|Phylogeny, ~1|Species_Latin, ~1|Study, ~1|Effect_ID), data = dat, test ="t",sparse =TRUE,control=list(optimizer="optim", optmethod="Nelder-Mead")#optmethod="BFGS") )}
# orchard plot#test <- mod_results(mod_sex1, mod = "Sex", group = "Study", data = dat)p1 <-orchard_plot(mod_sex1, mod ="Sex", xlab ="log response ratio (lnRR)", group ="Study",cb = F,angle =0)p1
Code
# creating a new variable Sex + Gonad because dat$Sex_Gonads <-paste(dat$Sex, dat$Gonads_removed, sep ="_")dat$Sex_Gonads[grep("NA", dat$Sex_Gonads)] <-NAdat$Sex_Gonads <-factor(dat$Sex_Gonads, levels =c("Female_No", "Female_Yes", "Male_Yes"),labels =c("Gonads removed \n(female)", "Gonads not removed \n(female)", "Gonads removed \n(male)") )mod_rem <-mod_func(formula =~ Sex_Gonads-1)summary(mod_rem)
# orchard plotp6 <-orchard_plot(mod_eff1, mod ="Effect_type", xlab ="log response ratio (lnRR)", group ="Study",cb = F) +scale_fill_manual(values =c("#D55E00", "#009E73")) +scale_colour_manual(values =c("#D55E00", "#009E73")) p6
Code
mod_effa <- mod_alt1 <-rma.mv(y2, V = V_matrix2,mod =~ Effect_type-1, random =list(~1|Species_Latin, ~1|Study, ~1|Effect_ID), data = dat, test ="t",sparse =TRUE,control=list(optimizer="optim", optmethod="Nelder-Mead"))summary(mod_effa)
Multivariate Meta-Analysis Model (k = 159; method: REML)
logLik Deviance AIC BIC AICc
-71.1533 142.3065 152.3065 167.5878 152.7039
Variance Components:
estim sqrt nlvls fixed factor
sigma^2.1 0.0179 0.1337 22 no Species_Latin
sigma^2.2 0.0176 0.1327 71 no Study
sigma^2.3 0.0024 0.0492 159 no Effect_ID
Test for Residual Heterogeneity:
QE(df = 157) = 587.4308, p-val < .0001
Test of Moderators (coefficients 1:2):
F(df1 = 2, df2 = 157) = 12.7018, p-val < .0001
Model Results:
estimate se tval df pval
Effect_typeMean or meadian \nlongevity 0.1538 0.0569 2.7030 157 0.0076
Effect_typeMortality \n(%) 0.3168 0.0674 4.6983 157 <.0001
ci.lb ci.ub
Effect_typeMean or meadian \nlongevity 0.0414 0.2661 **
Effect_typeMortality \n(%) 0.1836 0.4500 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Code
mod_effa1 <- mod_alt1 <-rma.mv(y2, V = V_matrix2,mod =~ Effect_type, random =list(~1|Species_Latin, ~1|Study, ~1|Effect_ID), data = dat, test ="t",sparse =TRUE,control=list(optimizer="optim", optmethod="Nelder-Mead"))summary(mod_effa1)
# different reference (gives whether male slope is significant)mod_sex_mat1 <-mod_func(formula =~relevel(Sex,ref ="Male")*Maturity_at_treatment_ordinal)summary(mod_sex_mat1)
# at least 10 in each group (sex * wild = male wild is too few - 4)mod_full <-mod_func(formula =~ Sex_Gonads + Sex*Controlled_treatments + Wild_or_semi_wild + Sex*Maturity_at_treatment_ordinal)summary(mod_full)
res_mod_full <-readRDS(file =here("Rdata","literature", "res_mod_full.rds"))# delta AIC = 2res_mod_full2<-subset(res_mod_full, delta <=2) #, recalc.weights=FALSE)# the best model according to the delta 2best2 <-mod_func(formula =~ Controlled_treatments + Wild_or_semi_wild)#summary(best2)# model varaged coeffisentsavg2 <-model.avg(res_mod_full2)summary(avg2) # similar to the orignal resulde
Multivariate Meta-Analysis Model (k = 159; method: REML)
logLik Deviance AIC BIC AICc
16.4273 -32.8546 -22.8546 -7.5734 -22.4573
Variance Components:
estim sqrt nlvls fixed factor
sigma^2.1 0.0170 0.1304 22 no Species_Latin
sigma^2.2 0.0138 0.1175 71 no Study
sigma^2.3 0.0080 0.0893 159 no Effect_ID
Test for Residual Heterogeneity:
QE(df = 157) = 1313.2195, p-val < .0001
Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 157) = 0.0057, p-val = 0.9397
Model Results:
estimate se tval df pval ci.lb ci.ub
intrcpt 0.3555 2.5932 0.1371 157 0.8911 -4.7666 5.4776
Year -0.0001 0.0013 -0.0758 157 0.9397 -0.0027 0.0025
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Code
# The function for leave-one-study-outdat$Study <-as.factor(dat$Study)LeaveOneOut_effectsize <-list()for(i in1:length(levels(dat$Study))){ dat1 <- dat[dat$Study !=levels(dat$Study)[i], ] V_matrix <-impute_covariance_matrix(vi = dat1$vi, cluster = dat1$Shared_control, r =0.5) LeaveOneOut_effectsize[[i]] <-rma.mv(yi, V = V_matrix,random =list(~1|Species_Latin, ~1|Study, ~1|Effect_ID), test ="t",sparse =TRUE,control=list(optimizer="optim", optmethod="BFGS"),data = dat1) }# writing function for extracting est, ci.lb, and ci.ub from all modelsest.func <-function(mod){ df <-data.frame(est = mod$b, lower = mod$ci.lb, upper = mod$ci.ub)return(df)}#using dplyr to form data frameMA_LOO <-lapply(LeaveOneOut_effectsize, function(x) est.func(x))%>% bind_rows %>%mutate(left_out =levels(dat$Study))saveRDS(MA_LOO,file =here("Rdata", "literature","MA_LOO.rds"))
# shared control # this does not seem to work#V_matrix <- make_VCV_matrix(dat, V= "vi", cluster = "Shared_control", obs = "Effect_ID")V_matrix <-impute_covariance_matrix(vi = rdat$vi, cluster = rdat$Shared_control, r =0.5)# meta-analysis basics# phylo modelrmod <-rma.mv(yi, V = V_matrix, mod =~1, random =list(~1|Species_Latin, ~1|Study, ~1|Effect_ID), data = rdat, test ="t",sparse =TRUE,control=list(optimizer="optim", optmethod="Nelder-Mead") )summary(rmod)
Multivariate Meta-Analysis Model (k = 40; method: REML)
logLik Deviance AIC BIC AICc
23.9404 -47.8807 -39.8807 -33.2265 -38.7043
Variance Components:
estim sqrt nlvls fixed factor
sigma^2.1 0.0018 0.0423 3 no Species_Latin
sigma^2.2 0.0089 0.0941 23 no Study
sigma^2.3 0.0027 0.0522 40 no Effect_ID
Test for Heterogeneity:
Q(df = 39) = 176.6345, p-val < .0001
Model Results:
estimate se tval df pval ci.lb ci.ub
0.0650 0.0380 1.7128 39 0.0947 -0.0118 0.1418 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# visualizing the resultorchard_plot(rmod, xlab ="log response ratio (lnRR)", group ="Study")
Code
rmod_sex <-rma.mv(yi, V = V_matrix, mod =~ Sex, random =list(~1|Species_Latin, ~1|Study, ~1|Effect_ID), data = rdat, test ="t",sparse =TRUE,control=list(optimizer="optim", optmethod="Nelder-Mead") )summary(rmod_sex)
# visualizing the resultorchard_plot(rmod_sex, mod ="Sex", xlab ="log response ratio (lnRR)", group ="Study")
Code
# two more type of agesrdat$lnAge_Trt <-log(rdat$Age_at_treatment_continuous)rdat$Age_ratio <- (rdat$Age_at_treatment_continuous/rdat$Day_to_matuarity)rdat$lnAge_ratio <-log(rdat$Age_at_treatment_continuous/rdat$Day_to_matuarity)# just pure effectrmod_mat <-rma.mv(yi, V = V_matrix, mod =~ lnAge_ratio*Sex, random =list(~1|Species_Latin, ~1|Study, ~1|Effect_ID), data = rdat, test ="t",sparse =TRUE,control=list(optimizer="optim", optmethod="Nelder-Mead") )summary(rmod_mat)
# visualizing the resultbubble_plot(rmod_mat, mod ="lnAge_ratio", group ="Study", by ="Sex", xlab ="ln(Treatment Day/Day to sexual maturity) [Rodent data only]", ylab ="ln(Response ratio)")
# variance covariance matrixV_matrix_long <-impute_covariance_matrix(vi = dat_long$vi, cluster = dat_long$Shared_control, r =0.5)# we can run - some heteroscad models# this does not improve modelmod_comp <-rma.mv(yi, V = V_matrix_long, mod =~ Comp_type -1, random =list(~1|Species_Latin, ~1|Study, ~1|Effect_ID, ~1|Obs ), test ="t",sparse =TRUE,data = dat_long)summary(mod_comp)
Multivariate Meta-Analysis Model (k = 170; method: REML)
logLik Deviance AIC BIC AICc
5.5127 -11.0253 0.9747 19.7184 1.4964
Variance Components:
estim sqrt nlvls fixed factor
sigma^2.1 0.0191 0.1381 15 no Species_Latin
sigma^2.2 0.0000 0.0000 34 no Study
sigma^2.3 0.0000 0.0000 85 no Effect_ID
sigma^2.4 0.0223 0.1492 170 no Obs
Test for Residual Heterogeneity:
QE(df = 168) = 1500.6613, p-val < .0001
Test of Moderators (coefficients 1:2):
F(df1 = 2, df2 = 168) = 0.1365, p-val = 0.8725
Model Results:
estimate se tval df pval ci.lb ci.ub
Comp_typeboth_normal 0.0171 0.0454 0.3758 168 0.7076 -0.0726 0.1068
Comp_typeone_castrated 0.0037 0.0450 0.0830 168 0.9340 -0.0851 0.0926
Comp_typeboth_normal
Comp_typeone_castrated
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# visualizing the resultorchard_plot(mod_comp, mod ="Comp_type", xlab ="log response ratio (lnRR)", group ="Study", cb = F)
Code
# naming factors# dat_long$Comp_type <- factor(dat_long$Comp_type, # levels = c("one_castrated", "both_normal"),# labels = c("one_castrated", "both_normal") )dat_long$Comp_type_Sex1 <-factor(dat_long$Comp_type_Sex, levels =c("one_castrated_Male", "both_normal_Male","one_castrated_Female", "both_normal_Female"),labels =c("Male sterlized/\nFemale normal","Male normal/\nFemale normal (B)","Male normal/\nFemale sterlized","Male normal/\nFemale normal (A)") )mod_comp_sex <-rma.mv(yi, V = V_matrix_long, mod =~ Comp_type_Sex1 -1 , random =list( ~1|Species_Latin, ~1|Study, ~1|Effect_ID, ~1|Obs ),data = dat_long, test ="t")summary(mod_comp_sex)
Multivariate Meta-Analysis Model (k = 170; method: REML)
logLik Deviance AIC BIC AICc
24.2082 -48.4164 -32.4164 -7.5205 -31.4992
Variance Components:
estim sqrt nlvls fixed factor
sigma^2.1 0.0191 0.1382 15 no Species_Latin
sigma^2.2 0.0000 0.0000 34 no Study
sigma^2.3 0.0000 0.0000 85 no Effect_ID
sigma^2.4 0.0142 0.1190 170 no Obs
Test for Residual Heterogeneity:
QE(df = 166) = 1158.6282, p-val < .0001
Test of Moderators (coefficients 1:4):
F(df1 = 4, df2 = 166) = 11.6896, p-val < .0001
Model Results:
estimate se tval df
Comp_type_Sex1Male sterlized/\nFemale normal 0.1297 0.0481 2.6949 166
Comp_type_Sex1Male normal/\nFemale normal (B) -0.0115 0.0486 -0.2370 166
Comp_type_Sex1Male normal/\nFemale sterlized -0.1063 0.0476 -2.2332 166
Comp_type_Sex1Male normal/\nFemale normal (A) 0.0518 0.0481 1.0774 166
pval ci.lb ci.ub
Comp_type_Sex1Male sterlized/\nFemale normal 0.0078 0.0347 0.2247 **
Comp_type_Sex1Male normal/\nFemale normal (B) 0.8129 -0.1075 0.0844
Comp_type_Sex1Male normal/\nFemale sterlized 0.0269 -0.2002 -0.0123 *
Comp_type_Sex1Male normal/\nFemale normal (A) 0.2829 -0.0432 0.1468
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Code
r2_ml(mod_comp_sex)
R2_marginal R2_conditional
0.1848997 0.6530464
Code
d0 <-orchard_plot(mod_comp_sex, mod ="Comp_type_Sex1", xlab ="log response ratio (lnRR)", group ="Study", cb = T, angle =90) +geom_vline(xintercept=2.5, size =0.2)d0
Code
# VCV matrixabs_V_matrix_long <-impute_covariance_matrix(vi = dat_long$abs_vi, cluster = dat_long$Shared_control, r =0.5)abs_mod_comp_sex0 <-rma.mv(abs_yi, V = abs_V_matrix_long, mod =~ Comp_type_Sex1 , random =list( ~1|Species_Latin, ~1|Study, ~1|Effect_ID, ~1|Obs ),data = dat_long, test ="t",sparse =TRUE)summary(abs_mod_comp_sex0)
Multivariate Meta-Analysis Model (k = 170; method: REML)
logLik Deviance AIC BIC AICc
95.4401 -190.8802 -174.8802 -149.9843 -173.9630
Variance Components:
estim sqrt nlvls fixed factor
sigma^2.1 0.0048 0.0690 15 no Species_Latin
sigma^2.2 0.0012 0.0350 34 no Study
sigma^2.3 0.0000 0.0000 85 no Effect_ID
sigma^2.4 0.0044 0.0662 170 no Obs
Test for Residual Heterogeneity:
QE(df = 166) = 929.3916, p-val < .0001
Test of Moderators (coefficients 2:4):
F(df1 = 3, df2 = 166) = 10.3634, p-val < .0001
Model Results:
estimate se tval df
intrcpt 0.1501 0.0283 5.3088 166
Comp_type_Sex1Male normal/\nFemale normal (B) 0.0139 0.0219 0.6366 166
Comp_type_Sex1Male normal/\nFemale sterlized 0.0995 0.0262 3.7999 166
Comp_type_Sex1Male normal/\nFemale normal (A) -0.0077 0.0262 -0.2939 166
pval ci.lb ci.ub
intrcpt <.0001 0.0943 0.2059 ***
Comp_type_Sex1Male normal/\nFemale normal (B) 0.5252 -0.0292 0.0571
Comp_type_Sex1Male normal/\nFemale sterlized 0.0002 0.0478 0.1511 ***
Comp_type_Sex1Male normal/\nFemale normal (A) 0.7692 -0.0594 0.0440
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Code
abs_mod_comp_sex <-rma.mv(abs_yi, V = abs_V_matrix_long, mod =~ Comp_type_Sex1 -1 , random =list( ~1|Species_Latin, ~1|Study, ~1|Effect_ID, ~1|Obs), data = dat_long, test ="t",sparse =TRUE)summary(abs_mod_comp_sex)
Multivariate Meta-Analysis Model (k = 170; method: REML)
logLik Deviance AIC BIC AICc
95.4401 -190.8802 -174.8802 -149.9843 -173.9630
Variance Components:
estim sqrt nlvls fixed factor
sigma^2.1 0.0048 0.0690 15 no Species_Latin
sigma^2.2 0.0012 0.0350 34 no Study
sigma^2.3 0.0000 0.0000 85 no Effect_ID
sigma^2.4 0.0044 0.0662 170 no Obs
Test for Residual Heterogeneity:
QE(df = 166) = 929.3916, p-val < .0001
Test of Moderators (coefficients 1:4):
F(df1 = 4, df2 = 166) = 21.0998, p-val < .0001
Model Results:
estimate se tval df
Comp_type_Sex1Male sterlized/\nFemale normal 0.1501 0.0283 5.3088 166
Comp_type_Sex1Male normal/\nFemale normal (B) 0.1640 0.0287 5.7199 166
Comp_type_Sex1Male normal/\nFemale sterlized 0.2496 0.0284 8.8009 166
Comp_type_Sex1Male normal/\nFemale normal (A) 0.1424 0.0282 5.0412 166
pval ci.lb ci.ub
Comp_type_Sex1Male sterlized/\nFemale normal <.0001 0.0943 0.2059 ***
Comp_type_Sex1Male normal/\nFemale normal (B) <.0001 0.1074 0.2206 ***
Comp_type_Sex1Male normal/\nFemale sterlized <.0001 0.1936 0.3055 ***
Comp_type_Sex1Male normal/\nFemale normal (A) <.0001 0.0866 0.1982 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Code
r2_ml(abs_mod_comp_sex)
R2_marginal R2_conditional
0.1548316 0.6429652
Code
# visualizing resultsd1 <-orchard_plot(abs_mod_comp_sex, mod ="Comp_type_Sex1", xlab ="absolute log response ratio (lnRR)", group ="Study", cb = T, angle =90) +geom_vline(xintercept=2.5, size =0.2)d1
Code
# femalefemale_dat_log <- dat_long %>%filter(Sex =="Female")f_dat_log<- female_dat_log %>%group_by(Effect_ID) %>%summarise(yi2 = abs_yi[2] - abs_yi[1],vi2 = abs_vi[1] + abs_vi[2] -0.5*sqrt(abs_vi[1]*abs_vi[2]),Species_Latin = Species_Latin[1],Study = Study[1],Shared_control = Shared_control[1])# variance covariance matrixV_matrix_long1 <-impute_covariance_matrix(vi = f_dat_log$vi2, cluster = f_dat_log$Shared_control, r =0.5)# we can run - some heteroscad models# this does not improve modelf_mod_long <-rma.mv(yi2, V = V_matrix_long1, random =list(~1|Species_Latin, ~1|Study, ~1|Effect_ID), data = f_dat_log, test ="t", sparse =TRUE)summary(f_mod_long)
Multivariate Meta-Analysis Model (k = 44; method: REML)
logLik Deviance AIC BIC AICc
23.2097 -46.4193 -38.4193 -31.3745 -37.3667
Variance Components:
estim sqrt nlvls fixed factor
sigma^2.1 0.0045 0.0672 13 no Species_Latin
sigma^2.2 0.0000 0.0000 28 no Study
sigma^2.3 0.0035 0.0595 44 no Effect_ID
Test for Heterogeneity:
Q(df = 43) = 168.9263, p-val < .0001
Model Results:
estimate se tval df pval ci.lb ci.ub
0.0904 0.0320 2.8227 43 0.0072 0.0258 0.1549 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
d3 <-orchard_plot(f_mod_long, xlab ="absolute log response ratio (lnRR)", group ="Study", cb = F, angle =90) +scale_fill_manual(values ="#999933") +scale_colour_manual(values ="#999933") +scale_x_discrete(labels ="Male normal/Female sterlized vs. \nMale normal/Female normal") +ylim(-0.9, 0.5)d3
Code
# pair figure male male_dat_log <- dat_long %>%filter(Sex =="Male")m_dat_log<- male_dat_log %>%group_by(Effect_ID) %>%summarise(yi2 = abs_yi[2] - abs_yi[1],vi2 = abs_vi[1] + abs_vi[2] -0.5*sqrt(abs_vi[1]*abs_vi[2]),Species_Latin = Species_Latin[1],Study = Study[1],Shared_control = Shared_control[1])# variance covariance matrixV_matrix_long2 <-impute_covariance_matrix(vi = m_dat_log$vi2, cluster = m_dat_log$Shared_control, r =0.5)# we can run - some hetero-scad models# this does not improve modelm_mod_long <-rma.mv(yi2, V = V_matrix_long2, random =list(~1|Species_Latin, ~1|Study, ~1|Effect_ID), data = m_dat_log, test ="t")summary(m_mod_long)
Multivariate Meta-Analysis Model (k = 41; method: REML)
logLik Deviance AIC BIC AICc
12.6521 -25.3041 -17.3041 -10.5486 -16.1613
Variance Components:
estim sqrt nlvls fixed factor
sigma^2.1 0.0157 0.1252 12 no Species_Latin
sigma^2.2 0.0003 0.0185 28 no Study
sigma^2.3 0.0019 0.0441 41 no Effect_ID
Test for Heterogeneity:
Q(df = 40) = 96.4195, p-val < .0001
Model Results:
estimate se tval df pval ci.lb ci.ub
-0.0221 0.0462 -0.4784 40 0.6350 -0.1154 0.0712
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# naming factorsdat_long$Comp_type <-factor(sdat_long$Comp_type, levels =c("both_castrated", "both_normal"),labels =c("Male sterlized/\nFemale sterlized","Male normal/\nFemale normal") )# VCV matrixV_matrix_long <-impute_covariance_matrix(vi = sdat_long$vi, cluster = sdat_long$Shared_control, r =0.5)# correlaiton matrix for phylogeny# tree <- read.tree(here("data/tree_rotl.tre"))# tree <- compute.brlen(tree)# cor_tree <- vcv(tree, corr = TRUE)# without heteroscedasticitymod_comp2 <-rma.mv(yi, V = V_matrix_long, mod =~ Comp_type -1, random =list( ~1|Species_Latin, ~1|Study, ~1|Effect_ID, ~1|Obs),data = sdat_long, test ="t")summary(mod_comp2)
Multivariate Meta-Analysis Model (k = 64; method: REML)
logLik Deviance AIC BIC AICc
24.7648 -49.5296 -37.5296 -24.7668 -36.0023
Variance Components:
estim sqrt nlvls fixed factor
sigma^2.1 0.0125 0.1118 10 no Species_Latin
sigma^2.2 0.0000 0.0000 25 no Study
sigma^2.3 0.0033 0.0571 32 no Effect_ID
sigma^2.4 0.0154 0.1242 64 no Obs
Test for Residual Heterogeneity:
QE(df = 62) = 2017.0006, p-val < .0001
Test of Moderators (coefficients 1:2):
F(df1 = 2, df2 = 62) = 0.1994, p-val = 0.8197
Model Results:
estimate se tval df
Comp_typeMale sterlized/\nFemale sterlized -0.0104 0.0482 -0.2156 62
Comp_typeMale normal/\nFemale normal -0.0264 0.0482 -0.5470 62
pval ci.lb ci.ub
Comp_typeMale sterlized/\nFemale sterlized 0.8300 -0.1067 0.0860
Comp_typeMale normal/\nFemale normal 0.5863 -0.1227 0.0700
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Code
# without heteroscedasticitymod_comp2b <-rma.mv(yi, V = V_matrix_long, mod =~ Comp_type, random =list( ~1|Species_Latin, ~1|Study, ~1|Effect_ID, ~1|Obs),data = sdat_long, test ="t")summary(mod_comp2b)
Multivariate Meta-Analysis Model (k = 64; method: REML)
logLik Deviance AIC BIC AICc
24.7648 -49.5296 -37.5296 -24.7668 -36.0023
Variance Components:
estim sqrt nlvls fixed factor
sigma^2.1 0.0125 0.1118 10 no Species_Latin
sigma^2.2 0.0000 0.0000 25 no Study
sigma^2.3 0.0033 0.0571 32 no Effect_ID
sigma^2.4 0.0154 0.1242 64 no Obs
Test for Residual Heterogeneity:
QE(df = 62) = 2017.0006, p-val < .0001
Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 62) = 0.2342, p-val = 0.6301
Model Results:
estimate se tval df pval
intrcpt -0.0104 0.0482 -0.2156 62 0.8300
Comp_typeMale normal/\nFemale normal -0.0160 0.0330 -0.4839 62 0.6301
ci.lb ci.ub
intrcpt -0.1067 0.0860
Comp_typeMale normal/\nFemale normal -0.0820 0.0500
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# with heteroscedasticitymod_comp2c <-rma.mv(yi, V = V_matrix_long, mod =~ Comp_type, random =list( ~1|Species_Latin, ~1|Study, ~Comp_type|Effect_ID),rho =0, struct ="HCS",data = sdat_long, test ="t")summary(mod_comp2c)
Multivariate Meta-Analysis Model (k = 64; method: REML)
logLik Deviance AIC BIC AICc
30.3689 -60.7377 -48.7377 -35.9749 -47.2105
Variance Components:
estim sqrt nlvls fixed factor
sigma^2.1 0.0057 0.0756 10 no Species_Latin
sigma^2.2 0.0000 0.0000 25 no Study
outer factor: Effect_ID (nlvls = 32)
inner factor: Comp_type (nlvls = 2)
estim sqrt k.lvl fixed level
tau^2.1 0.0058 0.0763 32 no Male sterlized/\nFemale sterlized
tau^2.2 0.0343 0.1851 32 no Male normal/\nFemale normal
rho 0.0000 yes
Test for Residual Heterogeneity:
QE(df = 62) = 2017.0006, p-val < .0001
Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 62) = 0.1620, p-val = 0.6887
Model Results:
estimate se tval df pval
intrcpt -0.0231 0.0330 -0.7005 62 0.4862
Comp_typeMale normal/\nFemale normal -0.0150 0.0374 -0.4025 62 0.6887
ci.lb ci.ub
intrcpt -0.0889 0.0428
Comp_typeMale normal/\nFemale normal -0.0897 0.0596
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Code
# Comparing models with and without heteroscedasticityAIC(mod_comp2, mod_comp2c)
# visualizing resultsf1 <-orchard_plot(mod_comp2c, mod ="Comp_type", xlab ="log response ratio (lnRR)", group ="Study", angle =90) +scale_fill_manual(values =c("#D55E00", "#009E73")) +scale_colour_manual(values =c("#D55E00", "#009E73")) f1
Code
# VCV matrixabs_V_matrix_long <-impute_covariance_matrix(vi = sdat_long$abs_vi, cluster = sdat_long$Shared_control, r =0.5)# with heteroscedasticitymod_comp3 <-rma.mv(abs_yi, V = abs_V_matrix_long, mod =~ Comp_type -1, random =list( ~1|Species_Latin, ~1|Study, ~Comp_type|Effect_ID),rho =0, struct ="HCS",data = sdat_long, test ="t")summary(mod_comp3)
Multivariate Meta-Analysis Model (k = 64; method: REML)
logLik Deviance AIC BIC AICc
46.7119 -93.4239 -81.4239 -68.6611 -79.8966
Variance Components:
estim sqrt nlvls fixed factor
sigma^2.1 0.0008 0.0283 10 no Species_Latin
sigma^2.2 0.0000 0.0000 25 no Study
outer factor: Effect_ID (nlvls = 32)
inner factor: Comp_type (nlvls = 2)
estim sqrt k.lvl fixed level
tau^2.1 0.0029 0.0540 32 no Male sterlized/\nFemale sterlized
tau^2.2 0.0256 0.1600 32 no Male normal/\nFemale normal
rho 0.0000 yes
Test for Residual Heterogeneity:
QE(df = 62) = 1423.9437, p-val < .0001
Test of Moderators (coefficients 1:2):
F(df1 = 2, df2 = 62) = 18.2633, p-val < .0001
Model Results:
estimate se tval df
Comp_typeMale sterlized/\nFemale sterlized 0.0844 0.0170 4.9548 62
Comp_typeMale normal/\nFemale normal 0.1547 0.0322 4.8113 62
pval ci.lb ci.ub
Comp_typeMale sterlized/\nFemale sterlized <.0001 0.0503 0.1184 ***
Comp_typeMale normal/\nFemale normal <.0001 0.0904 0.2190 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Code
# with heteroscedasticitymod_comp3b <-rma.mv(abs_yi, V = abs_V_matrix_long, mod =~ Comp_type, random =list( ~1|Species_Latin, ~1|Study, ~Comp_type|Effect_ID),rho =0, struct ="HCS",data = sdat_long, test ="t")summary(mod_comp3b)
Multivariate Meta-Analysis Model (k = 64; method: REML)
logLik Deviance AIC BIC AICc
46.7119 -93.4239 -81.4239 -68.6611 -79.8966
Variance Components:
estim sqrt nlvls fixed factor
sigma^2.1 0.0008 0.0283 10 no Species_Latin
sigma^2.2 0.0000 0.0000 25 no Study
outer factor: Effect_ID (nlvls = 32)
inner factor: Comp_type (nlvls = 2)
estim sqrt k.lvl fixed level
tau^2.1 0.0029 0.0540 32 no Male sterlized/\nFemale sterlized
tau^2.2 0.0256 0.1600 32 no Male normal/\nFemale normal
rho 0.0000 yes
Test for Residual Heterogeneity:
QE(df = 62) = 1423.9437, p-val < .0001
Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 62) = 5.0020, p-val = 0.0289
Model Results:
estimate se tval df pval
intrcpt 0.0844 0.0170 4.9548 62 <.0001
Comp_typeMale normal/\nFemale normal 0.0703 0.0314 2.2365 62 0.0289
ci.lb ci.ub
intrcpt 0.0503 0.1184 ***
Comp_typeMale normal/\nFemale normal 0.0075 0.1332 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Code
# this function does not work with heterogenious variance#r2_ml(mod_comp3b) # without heteroscedasticitymod_comp3c <-rma.mv(abs_yi, V = abs_V_matrix_long, mod =~ Comp_type, random =list( ~1|Species_Latin, ~1|Study, ~1|Effect_ID),#rho = 0, struct = "HCS",data = sdat_long, test ="t")# Comparing models with and without heteroscedasticityAIC(mod_comp3, mod_comp3c)
12-13 months oldStated that hair greying delayed and also there is rectal temperator data
NA
54
Jiang et al Biorvx 2023
140
60
60
12.5 months
12.5
Cognition
Cognition
extracted
decreased
Barnes maze test
Barnes Maze test_time to find correct hole
Yes
Sham
Male
Mus musculus
Mice
C57BL6
no
Laboratory
No
8 weeks
8.00
162.5326000
69.9328000
25.45105
13.07186
mean
sec
SEM
80.4832868
32.0193870
NA
NA
NA
NA
NA
10
6
fig 1E
12-13 months oldStated that hair greying delayed and also there is rectal temperator data
NA
55
Leffa 2013
76
12
12
18 months
18
Cognition
Cognition
extracted
Increased
inhibitory avoidance test
inhibitory avoidance test
yes
Sham
Female
Ratus ratus
Rat
wistar
no
Laboratory
No
3 months
12.00
4.1700000
9.3500000
0.59(lower)6.31 (upper)
3.72(lower)25.05(upper)
median
latency to step
interquartile interval
4.3630816
16.2700229
5.720
21.33
NA
NA
NA
12
12
Fig2
NA
NA
56
Hou 2013
63
5
5
12 months
12
Cognition
Cognition
checked
decreased
Morris water maze
morris water maze latency to find platform in probe test
yes
Sham
Female
Mus musculus
Mice
ICR
no
Laboratory
No
8 weeks
8.00
22.1600000
36.5000000
6.89
4.5
mean
s
SD
6.8900000
4.5000000
NA
NA
NA
NA
NA
10
10
Fig 1
NA
NA
57
Hou 2013
64
5
5
12 months
12
Cognition
Cognition
checked
Increased
Morris water maze
morris water mazeplatform crossings
yes
Sham
Female
Mus musculus
Mice
ICR
no
Laboratory
No
8 weeks
8.00
2.8900000
1.8500000
1.215
1.39
mean
n
SD
1.2150000
1.3900000
NA
NA
NA
NA
NA
10
10
fig 1
NA
NA
58
Hou 2013
65
6
6
12 months
12
Cognition
Cognition
checked
decreased
Morris water maze
morris water maze latency to find platform in probe test
yes
Sham
Male
Mus musculus
Mice
ICR
no
Laboratory
No
8 weeks
8.00
27.6600000
34.3200000
10.16
5.95
mean
s
SD
10.1600000
5.9500000
NA
NA
NA
NA
NA
10
10
Fig 1
NA
NA
59
Hou 2013
66
6
6
12 months
12
Cognition
Cognition
checked
Increased
Morris water maze
morris water mazeplatform crossings
yes
Sham
Male
Mus musculus
Mice
ICR
no
Laboratory
No
8 weeks
8.00
2.4240000
2.6500000
1.54
1.59
mean
n
SD
1.5400000
1.5900000
NA
NA
NA
NA
NA
10
10
Fig 1
NA
NA
60
Koebele 2023
71
20
20
early middle age 12 months
12
Cognition
Cognition
checked
Increased
Morris water maze
moris maze: probe trial NE (correct) quadrant
yes
Sham
Female
Ratus ratus
Rat
Fischer 344 CDF
no
Laboratory
No
5 months
22.00
0.4700000
0.4900000
0.059
0.046
mean
proportion
SE
0.1865744
0.1380000
NA
NA
NA
NA
NA
10
9
fig 4D
NA
NA
61
Koebele 2023
72
20
20
late middle age at 17 months
17
Cognition
Cognition
checked
Increased
Morris water maze
moris maze: probe trial NE (correct) quadrant
yes
Sham
Female
Ratus ratus
Rat
Fischer 344 CDF
no
Laboratory
No
5 months
22.00
0.3900000
0.3700000
0.04
0.051
mean
proportion
SE
0.1200000
0.1349333
NA
NA
NA
NA
NA
9
7
fig4F
NA
NA
62
galea 2018
42
22
22
14 months
14
Cognition
Cognition
checked
Increased
Morris water maze
Morris water maze (percentage of time in quadrant in probe trial)
yes
Sham
Female
Ratus ratus
Rat
sprague-Dawley
no
Laboratory
No
8 months
35.00
29.4000000
31.7000000
2.5
2.2
mean
%
SEM
7.9056942
6.9570109
NA
NA
NA
NA
NA
10
10
table 3
nulliparous
Data is also presented for a second test, reversal learning. Have not included this but could come under other memory tests? Stated that it is a test of cognitive flexibility
63
galea 2018
43
23
23
14 months
14
Cognition
Cognition
checked
Increased
Morris water maze
Morris water maze (percentage of time in quadrant in probe trial)
yes
Sham
Female
Ratus ratus
Rat
sprague-Dawley
no
Laboratory
No
8 months
35.00
28.5000000
34.3000000
4.6
2.9
mean
%
SEM
13.0107648
9.6182119
NA
NA
NA
NA
NA
8
11
table 3
primiparous
Data is also presented for a second test, reversal learning. Have not included this but could come under other memory tests? Stated that it is a test of cognitive flexibility
64
Itoh et al 2023
137
57
57
12-14 months
13
Cognition
Cognition
extracted
Increased
Morris water maze
Morris water maze probe trial time in correct quadrant
Yes
unclear
Male
Mus musculus
Mice
C57BL6
no
Laboratory
No
2 months old
8.00
37.5000000
39.5000000
36.101 (lower) 41.134 (upper)
36.38(lower) 41.28 (upper)
median
percentage in correct zone
IQR
3.8244681
3.7234043
5.033
4.90
NA
NA
NA
14
7
Fg 2
NA
NA
65
Itoh et al 2023
138
58
58
12-14 months
13
Cognition
Cognition
extracted
Increased
Morris water maze
Morris water maze probe trial time in correct quadrant
Yes
unclear
Female
Mus musculus
Mice
C57BL6
no
NA
NA
2 months old
8.00
34.9191770
25.8702356
33.81(lower) 44.08 (upper)
20.34 (lower)31.18(upper)
median
percentage in correct zone
IQR
7.7218045
8.1503759
10.270
10.84
NA
NA
NA
30
8
Fig 2
NA
NA
66
Borbelyova et al 2016
134
77
77
30 months
30
Cognition
Cognition
extracted
Increased
novel object recognition
Novel object recognition test
Yes
unclear
Male
Ratus ratus
Rat
wistar albino
no
Laboratory
No
Day 47
6.50
4.1732271
12.1028876
1.483910276
3.075325883
mean
difference exploring novel object
SEM
5.7471598
11.5068158
NA
NA
NA
NA
NA
15
14
Fig3C
NA
NA
67
Leffa 2013
75
12
12
18 months
18
Cognition
Cognition
checked
Increased
novel object recognition
novel object recognition
yes
Sham
Female
Ratus ratus
Rat
wistar
no
Laboratory
No
3 months
12.00
0.6780000
0.6660000
0.068
0.036
mean
recognition index
SE
0.2355589
0.1247077
NA
NA
NA
NA
NA
12
12
Fig1
NA
NA
68
Wang 2021
121
53
53
14-15months
14.5
Cognition
Cognition
checked
Increased
novel object recognition
novel object recognition
yes
Sham
Male
Mus musculus
Mice
C57Bl6/J
no
NA
NA
8months
35.00
0.2800000
0.3900000
0.02
0.029
mean
index
SEM
0.0565685
0.0820244
NA
NA
NA
NA
NA
8
8
fig5i
NA
NA
69
Wang 2021
126
54
54
18 month
18
Cognition
Cognition
checked
Increased
novel object recognition
novel recognition open field
Yes
Sham
Male
Mus musculus
Mice
C57Bl6/J
no
Laboratory
No
8 months
35.00
0.1700000
0.1900000
0.03
0.03
mean
index
S.E.M
0.0848528
0.0848528
NA
NA
NA
NA
NA
8
8
Fig S3E
NA
NA
70
Heikkinen 2004
52
10
10
24 months
24
Cognition
Cognition
checked
decreased
RAM memory test
RAM memory test: reference memory errors
yes
Sham
Female
Mus musculus
Mice
C57Bl6/J
no
Laboratory
No
5 months
22.00
111.9100000
115.7700000
6.53
3.85
mean
n
SEM
17.2767561
12.7690054
NA
NA
NA
NA
NA
7
11
fig 1 & n in text
NA
NA
71
Heikkinen 2004
55
10
10
24 months
24
Cognition
Cognition
checked
decreased
RAM memory test
RAM memory test: reference memory errors
yes
Sham
Female
Mus musculus
Mice
C57Bl6/J
no
NA
NA
5 months
22.00
111.3700000
115.0300000
7.32
5.23
mean
n
SEM
19.3668996
17.3459476
NA
NA
NA
NA
NA
7
11
fig2
NA
NA
72
Heikkinen 2004
53
10
10
24 months
24
Cognition
Cognition
checked
decreased
RAM memory test
RAM memory test: rworking memory errors
yes
Sham
Female
Mus musculus
Mice
C57Bl6/J
no
Laboratory
No
5 months
22.00
45.4300000
32.9800000
12.98
5.82
mean
n
SEM
34.3418520
19.3027563
NA
NA
NA
NA
NA
7
11
fig 1
NA
NA
73
Heikkinen 2004
56
10
10
24 months
24
Cognition
Cognition
checked
decreased
RAM memory test
RAM memory test: rworking memory errors
yes
Sham
Female
Mus musculus
Mice
C57Bl6/J
no
NA
NA
5 months
22.00
46.3400000
33.9100000
12.34
5.7
mean
n
SEM
32.6485712
18.9047613
NA
NA
NA
NA
NA
7
11
fig2
NA
NA
74
Heikkinen 2004
54
10
10
24 months
24
Cognition
Cognition
checked
Increased
T maze test
T maze total correct choices
yes
Sham
Female
Mus musculus
Mice
C57Bl6/J
no
Laboratory
No
5 months
22.00
35.9300000
29.3750000
0.94
1.87
mean
n
SEM
2.4870062
6.2020884
NA
NA
NA
NA
NA
7
11
fig 1
NA
NA
75
Koebele 2023
69
20
20
early middle age 12 months
12
Cognition
Cognition
checked
decreased
water radial arm maze test
water radial arm maze: delayed memory retention number of errors
yes
Sham
Female
Ratus ratus
Rat
Fischer 344 CDF
no
Laboratory
No
5 months
22.00
1.9100000
1.5400000
0.525
0.395
mean
number
SE
1.5750000
1.1850000
NA
NA
NA
NA
NA
9
9
fig2H
Data is also presented for a
NA
76
Koebele 2023
67
20
20
early middle age 12 months
12
Cognition
Cognition
checked
decreased
water radial arm maze test
water radial arm maze: WMI errors maximum working memory load
yes
Sham
Female
Ratus ratus
Rat
Fischer 344 CDF
no
Laboratory
No
5 months
22.00
1.6060000
1.3240000
0.28
0.2
mean
number
SE
0.8854377
0.6000000
NA
NA
NA
NA
NA
10
9
fig2H
Data is also presented for a
NA
77
Koebele 2023
68
20
20
late middle age at 17 months
17
Cognition
Cognition
checked
decreased
water radial arm maze test
water radial arm maze: WMI errors maximum working memory load
yes
Sham
Female
Ratus ratus
Rat
Fischer 344 CDF
no
Laboratory
No
5 months
22.00
1.5300000
1.2800000
0.22
0.19
mean
number
SE
0.6600000
0.5026927
NA
NA
NA
NA
NA
9
7
fig2J
NA
NA
78
Koebele 2023
70
20
20
late middle age at 17 months
17
Cognition
Cognition
checked
decreased
water radial arm maze test
water radial arm maze:delayed memory retention number of errors
yes
Sham
Female
Ratus ratus
Rat
Fischer 344 CDF
no
Laboratory
No
5 months
22.00
2.0900000
1.3500000
0.347
0.66
mean
number
SE
1.0410000
1.7461959
NA
NA
NA
NA
NA
9
7
fig2J
NA
NA
79
Itoh et al 2023
139
59
59
12-14 months
13
Cognition
Cognition
extracted
Increased
Y maze testing
Y maze testing
Yes
unclear
Female
Mus musculus
Mice
C57BL6
no
NA
NA
2 months old
8.00
57.1200000
46.1700000
53.06(lower)72.71 (upper)
39.45(lower)50.19(upper_
median
percentage spontaneous alteration
IQR
14.9657273
8.1797411
19.650
10.74
NA
NA
NA
13
16
Fig 2
NA
NA
80
Zakeri et al 2019
148
63
63
23 months
23
Cognition
Cognition
extracted
Increased
Y maze testing
Y maze testing
Yes
Sham
Female
Mus musculus
Mice
NMRI
no
Laboratory
No
10 months
43.00
68.5712498
23.4193649
5.259502581
8.499296105
mean
percentage spontaneous alteration
S.E.M
11.7606053
19.0050039
NA
NA
NA
NA
NA
5
5
Fig 4
NA
NA
81
Zakeri et al 2019
149
64
63
23 months
23
Cognition
Cognition
extracted
Increased
Y maze testing
Y maze testing
Yes
intact
Female
Mus musculus
Mice
NMRI
no
Laboratory
No
10 months
43.00
63.7760050
23.4193649
13.33583607
8.499296105
mean
percentage spontaneous alteration
S.E.M
26.6716721
19.0050039
NA
NA
NA
NA
NA
4
5
Fig 4
NA
NA
82
Heinze-Milne 2021
57
50
50
18 months
18
Frailty assessment
Frailty
extracted
decreased
Frailty score
Frailty score
yes
Sham
Male
Mus musculus
Mice
C57Bl6/J
no
NA
NA
4 weeks
4.00
0.2239073
0.1773100
0.036645285
0.04081906
mean
frailty score
95% confidence interval (whole interval)
0.0093483
0.0104130
NA
NA
NA
NA
NA
10
11
fig 2
extracted mean and CI from figure
sham points: .287, .255, .246, .206,.19; points OX .22, .19, .17, .15, .12, .05, .18, .2, .23 but points # don't tally to n=10 or 11
83
de la Fuente 2004
30
34
34
18 months
18
Immune Function
Immune Function
reextracted
Increased
T cell function test
T cells function test in vitro; IL2 axillary leu w/ConA
yes
Sham
Female
Ratus ratus
Rat
wistar
no
NA
NA
12 months
52.00
435.4564033
52.2479564
95.74250681
12.07425068
mean
pg/ml
Sd
95.7425068
12.0742507
NA
NA
NA
NA
NA
6
6
Fig 7b
IL2 production mostly from T cellsin axi and spleen ChatGpt
why does it say chat gtp in adjacent cell
84
de la Fuente 2004
31
34
34
22 months
22
Immune Function
Immune Function
reextracted
Increased
T cell function test
T cells function test in vitro; IL2 axillary leu w/ConA
yes
Sham
Female
Ratus ratus
Rat
wistar
no
NA
NA
12 months
52.00
213.7772480
51.7540872
55.05790191
20.45299728
mean
pg/ml
Sd
55.0579019
20.4529973
NA
NA
NA
NA
NA
6
4
Fig 7b
IL2 production mostly from T cellsin axi and spleen ChatGpt
why does it say chat gtp in adjacent cell
85
de la Fuente 2004
32
34
34
24 months
24
Immune Function
Immune Function
reextracted
Increased
T cell function test
T cells function test in vitro; IL2 spleen leu w/ConA
yes
Sham
Female
Ratus ratus
Rat
wistar
no
NA
NA
12 months
52.00
367.5408719
139.8160763
76.72002725
44.85694823
mean
pg/ml
Sd
76.7200272
44.8569482
NA
NA
NA
NA
NA
6
5
Fig 7b
NA
NA
86
de la Fuente 2004
27
34
34
18 months
18
Immune Function
Immune Function
reextracted
Increased
T cell function test
T cells function test in vitro; Stimulation axillary leu w/ConA
yes
Sham
Female
Ratus ratus
Rat
wistar
no
NA
NA
12 months
52.00
446.5245870
180.4184182
215.661355
69.21497703
mean
index
Sd
215.6613550
69.2149770
NA
NA
NA
NA
NA
6
6
Fig 7a
Stim on ConA specific to Tcells in axi and spleen ChatGpt
NA
87
de la Fuente 2004
28
34
34
22 months
22
Immune Function
Immune Function
reextracted
Increased
T cell function test
T cells function test in vitro; Stimulation axillary leu w/ConA
yes
Sham
Female
Ratus ratus
Rat
wistar
no
NA
NA
12 months
52.00
141.7538371
138.0877896
54.25750318
92.28663603
mean
index
Sd
54.2575032
92.2866360
NA
NA
NA
NA
NA
6
4
Fig 7a
Stim on ConA specific to Tcells in axi and spleen ChatGpt
NA
88
de la Fuente 2004
29
34
34
24 months
24
Immune Function
Immune Function
reextracted
Increased
T cell function test
T cells function test in vitro; Stimulation axillary leu w/ConA
yes
Sham
Female
Ratus ratus
Rat
wistar
no
NA
NA
12 months
52.00
584.5146153
438.9480888
182.2270017
185.4531235
mean
index
Sd
182.2270017
185.4531235
NA
NA
NA
NA
NA
6
5
Fig 7a
Stim on ConA specific to Tcells in axi and spleen ChatGpt
NA
89
Apelo 2020
2
7
7
12 months
12
activity, energetics and metabolism
Metabolism
Yes
Increased
Energy expenditure
energy expenditure dark
yes
Sham
Female
Mus musculus
Mice
C57Bl6/J
no
Laboratory
No
3.5weeks
3.50
19.5500000
15.1600000
1.071
0.94
mean
kcal/kg/hr
SEM
3.3867994
2.9725410
NA
NA
NA
NA
NA
10
10
fig 4
Just dark period data used as this is when animals are active
Mice WT of Rictor flox mice
90
Apelo 2020
4
8
8
12 months
12
activity, energetics and metabolism
Metabolism
Yes
Increased
Energy expenditure
energy expenditure dark
yes
Sham
Male
Mus musculus
Mice
C57Bl6/J
no
Laboratory
No
3.5weeks
3.50
14.7600000
14.8900000
0.66
0.8
mean
kcal/kg/hr
SEM
2.1889724
2.5298221
NA
NA
NA
NA
NA
11
10
fig 4
Just dark period data used as this is when animals are active
mod1 <-rma.mv(yi = yi, V = VCV, mod =~ Sex -1, random =list(~1|Strain, ~1|Study, ~1|Effect_ID), #struct = "DIAG",data = dat, test ="t",sparse =TRUE,control=list(optimizer="optim", optmethod="BFGS"))summary(mod1)
# visualizing the resultmod_healthspan_sex <-mod_results(mod1, group ="Study", mod ="Sex", data = dat)#saveRDS(mod_healthspan_sex, here("Rdata", "fig", "mod_healthspan_sex.rds"))#mod_healthspan_sex <- readRDS(here("Rdata", "fig", "mod_healthspan_sex.rds")) orchard_plot(mod_healthspan_sex, mod ="Sex", xlab ="log response ratio (lnRR)", group ="Study", cb = F) # colour = T)
Code
# attr(lm_result, "class") <- NULL# orchard plotmain <-mod_results(mod, group ="Study")attr(main, "class") <-NULLmain$mod_table$name <-gsub("Intrcpt", "Overall", main$mod_table$name)main$mod_table$name <-factor(main$mod_table$name)main$data$moderator <-gsub("Intrcpt", "Overall", main$data$moderator)main$data$moderator <-factor(main$data$moderator)class(main) <-c("orchard", "data.frame")p_overall <-orchard_plot(main, xlab ="log response ratio (lnRR)", angle =0, group ="Study") +ylim(-2.2,2)sex_diff <-mod_results(mod1, mod ="Sex", group ="Study")p_sex_diff <-orchard_plot(sex_diff, mod ="Sex", xlab ="log response ratio (lnRR)", group ="Study", angle =0, cb = F) +ylim(-2.2,2)combined <-submerge(sex_diff, main)# changing the name (intercept) to:# TODO - this should be done in the orchard_plot functioncombined$mod_table$name <-gsub("Intrcpt", "Overall", combined$mod_table$name)combined$mod_table$name <-factor(combined$mod_table$name)combined$data$moderator <-gsub("Intrcpt", "Overall", combined$data$moderator)orchard_plot(combined,xlab ="log response ratio (lnRR)", group ="Study", angle =0, cb = F) +scale_colour_manual(values =rev(c("#999999", "#88CCEE", "#CC6677"))) +scale_fill_manual(values =rev(c("#999999", "#88CCEE", "#CC6677")))
Code
mod2 <-rma.mv(yi = yi, V = VCV, mod =~ Sub.measure -1, random =list(~1|Strain, ~1|Study, ~1|Effect_ID), data = dat, test ="t",sparse =TRUE,control=list(optimizer="optim", optmethod="BFGS"))summary(mod2)
dat$Effective_N <-1/dat$Sample_size_experimental +1/dat$Sample_size_controlegger_hs <-rma.mv(yi = yi, V = vi, mod =~sqrt(Effective_N), random =list(~1|Strain, ~1|Study, ~1|Effect_ID), data = dat, test ="t",sparse =TRUE,control=list(optimizer="optim", optmethod="BFGS"))summary(egger_hs)
dat$Year <-as.numeric(str_extract(as.character(dat$Study),"[:digit:][:digit:][:digit:][:digit:]"))decline <-rma.mv(yi = yi, V = vi, mod =~ Year, random =list(~1|Strain, ~1|Study, ~1|Effect_ID), data = dat, test ="t",sparse =TRUE,control=list(optimizer="optim", optmethod="BFGS"))summary(decline)
Multivariate Meta-Analysis Model (k = 194; method: REML)
logLik Deviance AIC BIC AICc
-140.0392 280.0784 290.0784 306.3659 290.4010
Variance Components:
estim sqrt nlvls fixed factor
sigma^2.1 0.0220 0.1484 23 no Strain
sigma^2.2 0.0000 0.0011 48 no Study
sigma^2.3 0.0585 0.2418 194 no Effect_ID
Test for Residual Heterogeneity:
QE(df = 192) = 328.9033, p-val < .0001
Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 192) = 2.7164, p-val = 0.1010
Model Results:
estimate se tval df pval ci.lb ci.ub
intrcpt 6.8728 4.1175 1.6692 192 0.0967 -1.2485 14.9941 .
Year -0.0034 0.0021 -1.6482 192 0.1010 -0.0075 0.0007
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Code
# The function for leave-one-study-outdat$Study <-as.factor(dat$Study)LeaveOneOut_effectsize <-list()for(i in1:length(levels(dat$Study))){ dat1 <- dat[dat$Study !=levels(dat$Study)[i], ] LeaveOneOut_effectsize[[i]] <-rma.mv(yi = yi, V = vi, random =list(~1|Strain, ~1|Study, ~1|Effect_ID), data = dat1, test ="t",sparse =TRUE,control=list(optimizer="optim", optmethod="BFGS") )}# writing function for extracting est, ci.lb, and ci.ub from all modelsest.func <-function(mod){ df <-data.frame(est = mod$b, lower = mod$ci.lb, upper = mod$ci.ub)return(df)}#using dplyr to form data frameMA_LOO <-lapply(LeaveOneOut_effectsize, function(x) est.func(x))%>% bind_rows %>%mutate(left_out =levels(dat$Study))saveRDS(MA_LOO,file =here("Rdata", "literature","MA_LOO2.rds"))
Multivariate Meta-Analysis Model (k = 82; method: REML)
logLik Deviance AIC BIC AICc
47.0117 -94.0234 -80.0234 -63.5264 -78.4234
Variance Components:
estim sqrt nlvls fixed factor R
sigma^2.1 0.0183 0.1351 20 no species no
sigma^2.2 0.0041 0.0642 20 no phylogeny yes
sigma^2.3 0.0051 0.0714 82 no obs_id no
Test for Residual Heterogeneity:
QE(df = 78) = 780.6119, p-val < .0001
Test of Moderators (coefficients 1:4):
F(df1 = 4, df2 = 78) = 6.6866, p-val = 0.0001
Model Results:
estimate se tval df
contraceptionF contraceptive/\nM contraceptive 0.0367 0.0644 0.5701 78
contraceptionF contraceptive/\nM normal 0.1080 0.0646 1.6708 78
contraceptionF normal/\nM contraceptive -0.0492 0.0631 -0.7799 78
contraceptionF normal/\nM normal 0.0518 0.0608 0.8526 78
pval ci.lb ci.ub
contraceptionF contraceptive/\nM contraceptive 0.5703 -0.0915 0.1649
contraceptionF contraceptive/\nM normal 0.0988 -0.0207 0.2366 .
contraceptionF normal/\nM contraceptive 0.4378 -0.1747 0.0764
contraceptionF normal/\nM normal 0.3965 -0.0692 0.1728
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Code
round(r2_ml(mod_comb)*100, 5)
R2_marginal R2_conditional
9.41548 83.17844
Code
#robust(mod_comb, cluster = species) orchard_plot(mod_comb, mod ="contraception",xlab ="log response ratio (lnRR)", group ="species", g =FALSE, angle =45)
Code
all_models(mod_comb, mod ="contraception")
Fixed effect
Estimate
Lower CI [0.025]
Upper CI [0.975]
P value
Lower PI [0.025]
Upper PI [0.975]
F contraceptive/ M contraceptive
0.037
-0.091
0.165
0.570
-0.317
0.391
F contraceptive/ M normal
0.108
-0.021
0.237
0.099
-0.246
0.462
F normal/ M contraceptive
-0.049
-0.175
0.076
0.438
-0.402
0.304
F normal/ M normal
0.052
-0.069
0.173
0.397
-0.300
0.403
F contraceptive/ M contraceptive-F contraceptive/ M normal
0.071
0.009
0.134
0.026
-0.260
0.402
F contraceptive/ M contraceptive-F normal/ M contraceptive
-0.086
-0.148
-0.024
0.007
-0.417
0.245
F contraceptive/ M contraceptive-F normal/ M normal
0.015
-0.043
0.073
0.612
-0.315
0.345
F contraceptive/ M normal-F normal/ M contraceptive
Multivariate Meta-Analysis Model (k = 82; method: REML)
logLik Deviance AIC BIC AICc
78.3730 -156.7461 -142.7461 -126.2491 -141.1461
Variance Components:
estim sqrt nlvls fixed factor R
sigma^2.1 0.0009 0.0296 20 no species no
sigma^2.2 0.0015 0.0390 20 no phylogeny yes
sigma^2.3 0.0035 0.0591 82 no obs_id no
Test for Residual Heterogeneity:
QE(df = 78) = 384.1271, p-val < .0001
Test of Moderators (coefficients 1:4):
F(df1 = 4, df2 = 78) = 5.7942, p-val = 0.0004
Model Results:
estimate se tval df
contraceptionF contraceptive/\nM contraceptive 0.1451 0.0383 3.7864 78
contraceptionF contraceptive/\nM normal 0.1852 0.0389 4.7650 78
contraceptionF normal/\nM contraceptive 0.1199 0.0366 3.2741 78
contraceptionF normal/\nM normal 0.1240 0.0342 3.6227 78
pval ci.lb ci.ub
contraceptionF contraceptive/\nM contraceptive 0.0003 0.0688 0.2214 ***
contraceptionF contraceptive/\nM normal <.0001 0.1078 0.2626 ***
contraceptionF normal/\nM contraceptive 0.0016 0.0470 0.1928 **
contraceptionF normal/\nM normal 0.0005 0.0559 0.1922 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Code
round(r2_ml(mod_comb_a)*100, 5)
R2_marginal R2_conditional
10.38107 46.81997
Code
#robust(mod_comb_a, cluster = species) orchard_plot(mod_comb_a, mod ="contraception",xlab ="absolute log response ratio (lnRR)", group ="species", g =FALSE, angle =45)
Code
all_models(mod_comb_a, mod ="contraception", type ="abs")
Fixed effect
Estimate
Lower CI [0.025]
Upper CI [0.975]
P value
Lower PI [0.025]
Upper PI [0.975]
F contraceptive/ M contraceptive
0.145
0.069
0.221
0.000
-0.026
0.316
F contraceptive/ M normal
0.185
0.108
0.263
0.000
0.014
0.357
F normal/ M contraceptive
0.120
0.047
0.193
0.002
-0.049
0.289
F normal/ M normal
0.124
0.056
0.192
0.001
-0.043
0.291
F contraceptive/ M contraceptive-F contraceptive/ M normal
0.040
-0.012
0.093
0.134
-0.119
0.199
F contraceptive/ M contraceptive-F normal/ M contraceptive
-0.025
-0.076
0.026
0.335
-0.184
0.134
F contraceptive/ M contraceptive-F normal/ M normal
-0.021
-0.069
0.027
0.389
-0.179
0.137
F contraceptive/ M normal-F normal/ M contraceptive
-0.065
-0.118
-0.013
0.015
-0.225
0.094
F contraceptive/ M normal-F normal/ M normal
-0.061
-0.111
-0.011
0.016
-0.220
0.097
F normal/ M contraceptive-F normal/ M normal
0.004
-0.041
0.049
0.858
-0.153
0.161
23 Pre-post puberty analysis
This analysis compares the effect size of the lifespan extension before and after puberty.
23.1 Loading data and obtaining effect isze
Code
#dat0 <- read_csv(here("data", "zoo", "timing_male.csv"), na = c("", "NA"))dat0 <-read_csv(here("data", "zoo", "resultsBaSTAbefAftMatur.csv"), na =c("", "NA"))# phylogeny# read RData#dford load(here("Rdata", "zoo", "maxCredTreeMammals.RData"))tree <- maxCred #read.tree(here("data", "zoo", "tree_zoo.tre"))# taxonomytax <-read.csv(here("data", "zoo", "species_merge_list.csv"))dat0 %>%left_join(tax, by =c("Species"="ZIMSSpecies")) -> dat_full# talking out species with no data (Pseudocheirus peregrinus = likely to be mistaks in data)# dat_full %>% filter(Species != "Chrysocyon brachyurus" &# Species != "Crocuta crocuta" &# Species != "Neofelis nebulosa" &# Species != "Panthera uncia" &# Species != "Pseudocheirus peregrinus") %>% # mutate(phylogeny = gsub(" ", "_", vertlife.species)) -> datdat_full %>%mutate(phylogeny =gsub(" ", "_", vertlife.treename)) -> dat# adding Cervus canadensis# dat$vertlife.species[which(dat$Species == "Cervus canadensis")] <-"Cervus canadensis"# dat$phylogeny[which(dat$Species == "Cervus canadensis")] <-"Cervus_canadensis"# fixing species names#dat$Species[dat$Species == "Equus asinus"] <- "Equus_africanus"dat$Species[dat$Species =="Aonyx cinereus"] <-"Aonyx cinerea"#dat$Species[dat$Species == "Bubalus bubalis"] <- "Bubalus arnee"# life span data to_drop <- tree$tip.label[which(!(tree$tip.label %in%unique(dat$phylogeny)))]tree <-drop.tip(tree, to_drop)# checking the number of spp#length(tree$tip.label)tree <-as.ultrametric(tree)#tree <- compute.brlen(tree)cor_tree <-vcv(tree, corr =TRUE)missing <-which(is.na(dat$phylogeny))dat$Species[missing]
#robust(mod_all, cluster = species) orchard_plot(mod_np, xlab ="lnRR (all)", group ="species", g =FALSE)
Code
# phlo - primates as a separate group mod_np2 <-rma.mv(yi, V = vi,mod =~ Primate,random =list(~1|species),data = dat,test ="t",sparse =TRUE)summary(mod_np2)
#robust(mod_all, cluster = species) orchard_plot(mod_np2, mod ="Primate", xlab ="lnRR (all)", group ="species", g =FALSE)
24 Extra analysis
This extra analysis compares the impact of group living and testes mass on the lifespan extension.
24.1 Loading data and effect size calculation
Code
# loading datadat0 <-read.csv(here("data", "zoo", "extra.csv"), na =c("", "NA"))load(here("Rdata", "zoo", "maxCredTreeMammals.RData"))tree <- maxCred #read.tree(here("data", "zoo", "tree_zoo.tre"))# taxonomytax <-read.csv(here("data", "zoo", "species_merge_list.csv"))dat0 %>%left_join(tax, by =c("Species"="ZIMSSpecies")) -> dat_full# talking out species with no data (Pseudocheirus peregrinus = likely to be mistaks in data)# dat_full %>% filter(Species != "Chrysocyon brachyurus" &# Species != "Crocuta crocuta" &# Species != "Neofelis nebulosa" &# Species != "Panthera uncia" &# Species != "Pseudocheirus peregrinus") %>% # mutate(phylogeny = gsub(" ", "_", vertlife.species)) -> datdat_full %>%mutate(phylogeny =gsub(" ", "_", vertlife.treename)) -> dat# take out NA which at Species columndat <- dat[!is.na(dat$Species),]# take out or filter out Macaca leonina (Species)dat <- dat[dat$Species !="Macaca leonina", ]# life span data to_drop <- tree$tip.label[which(!(tree$tip.label %in%unique(dat$phylogeny)))]tree <-drop.tip(tree, to_drop)# checking the number of spp#length(tree$tip.label)tree <-as.ultrametric(tree)#tree <- compute.brlen(tree)cor_tree <-vcv(tree, corr =TRUE)missing <-which(is.na(dat$phylogeny))dat$Species[missing]
Multivariate Meta-Analysis Model (k = 59; method: REML)
logLik Deviance AIC BIC AICc
28.5773 -57.1547 -47.1547 -37.0279 -45.9547
Variance Components:
estim sqrt nlvls fixed factor R
sigma^2.1 0.0114 0.1067 59 no species no
sigma^2.2 0.0000 0.0000 59 no phylogeny yes
Test for Residual Heterogeneity:
QE(df = 56) = 159.9245, p-val < .0001
Test of Moderators (coefficients 2:3):
QM(df = 2) = 1.8152, p-val = 0.4035
Model Results:
estimate se zval pval ci.lb ci.ub
intrcpt 0.1689 0.0953 1.7720 0.0764 -0.0179 0.3558 .
lntestemass 0.0261 0.0194 1.3468 0.1781 -0.0119 0.0641
lnbodymass -0.0164 0.0146 -1.1228 0.2615 -0.0450 0.0122
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Code
bubble_plot(mod_extra2, xlab ="log(Teste mass)", mod ="lntestemass", group ="species", g =FALSE)
26 PART IV: ANALYSIS OF CAUSES OF DEATH ON ZOO DATA
26.1 Loading data
Code
# main datadat0 <-read_csv(here("data", "zoo2", "causeDeathAll.csv"), na =c("", "NA"))# turning character strings into factorsdat0 <- dat0 %>%mutate(across(where(is.character), as.factor))# length(unique(dat0$Species)) # the number of species 131# phylogenytree <-read.tree(here("data", "zoo", "tree_zoo.tre"))# taxonomytax <-read.csv(here("data", "zoo", "vertlife_taxonomy_translation_table.csv"))# there are missing species in the taxonomy tabledat0 %>%left_join(tax, by =c("Species"="zims.species")) %>%# make character strings into factorsmutate(across(where(is.character), as.factor)) -> dat_fulldat <- dat_fulldat$Phylogeny <-gsub(" ", "_", dat$vertlife.species)#life span datato_drop <- tree$tip.label[which(!(tree$tip.label %in%unique(dat$Phylogeny)))]tree <-drop.tip(tree, to_drop)# checking the number of spp#length(tree$tip.label)tree <-compute.brlen(tree)#tree <- compute.brlen(tree)cor_tree <-vcv(tree, corr =TRUE)# samller data set which does not have NA in dat$Phylogenydat <- dat %>%filter(!is.na(Phylogeny))# Effect_ID is the unique identifier for the effectdat$Effect_ID <-factor(1:nrow(dat))# creating a variable combining Sex and Typedat$Sex_Type <-as.factor(paste0(dat$Sex, "_", dat$Type))
########## Trauma########## Lowdat_trauma <-escalc(measure ="RD", ai = Contra_Trauma_Low*Contra_Trauma_N, bi = (1-Contra_Trauma_Low)*Contra_Trauma_N, ci = noContra_Trauma_Low*noContra_Trauma_N,di = (1-noContra_Trauma_Low)*noContra_Trauma_N,var.names =c("yi_trauma_low", "vi_trauma_low"),data = dat)# Meddat_trauma <-escalc(measure ="RD", ai = Contra_Trauma_Med*Contra_Trauma_N, bi = (1-Contra_Trauma_Med)*Contra_Trauma_N, ci = noContra_Trauma_Med*noContra_Trauma_N,di = (1-noContra_Trauma_Med)*noContra_Trauma_N,var.names =c("yi_trauma_med", "vi_trauma_med"),data = dat_trauma)# Uppdat_trauma <-escalc(measure ="RD", ai = Contra_Trauma_Upp*Contra_Trauma_N, bi = (1-Contra_Trauma_Upp)*Contra_Trauma_N, ci = noContra_Trauma_Upp*noContra_Trauma_N,di = (1-noContra_Trauma_Upp)*noContra_Trauma_N,var.names =c("yi_trauma_upp", "vi_trauma_upp"),data = dat_trauma)dat_trauma %>%filter(Contra_Trauma_N >0) -> dat_trauma# create a long format of the data using these 3 types of effect sizes (low, med, upp) yi and vi are the effect size and variance of the effect sizedat_long_trauma <- dat_trauma %>%select(Effect_ID, Species, Phylogeny, Sex_Type, Sex, yi_trauma_low, vi_trauma_low, yi_trauma_med, vi_trauma_med, yi_trauma_upp, vi_trauma_upp) %>%pivot_longer(cols =c(yi_trauma_low, yi_trauma_med, yi_trauma_upp, vi_trauma_low, vi_trauma_med, vi_trauma_upp), names_to =c(".value", "type"), names_pattern ="(yi|vi)_(.*)")dat_long_trauma <- dat_long_trauma %>%filter(!is.na(yi))str(dat_long_trauma)
dat_long_chronic$type <-factor(dat_long_chronic$type, levels =rev(c("chronic_low", "chronic_med", "chronic_upp")),labels =rev(c("Lower", "Median", "Upper")) )# effect size level IDdat_long_chronic$Effect_ID2 <-factor(1:nrow(dat_long_chronic))# VCVVCV <-vcalc(dat_long_chronic$vi, cluster = dat_long_chronic$Effect_ID, obs = dat_long_chronic$Effect_ID2, data = dat_long_chronic, rho =0.5)# meta-analysis using dat_longmod_chronic <-rma.mv(yi = yi, V = VCV, random =list(~1|Species,~1|Phylogeny,~1|Effect_ID2), R =list(Phylogeny = cor_tree), data = dat_long_chronic,method="REML", test ="t",dfs ="contain",control=list(optimizer="optim", optmethod="Nelder-Mead"))summary(mod_chronic)
Multivariate Meta-Analysis Model (k = 234; method: REML)
logLik Deviance AIC BIC AICc
114.5739 -229.1478 -221.1478 -207.3437 -220.9724
Variance Components:
estim sqrt nlvls fixed factor R
sigma^2.1 0.0000 0.0000 61 no Species no
sigma^2.2 0.0000 0.0000 61 no Phylogeny yes
sigma^2.3 0.0000 0.0000 234 no Effect_ID2 no
Test for Heterogeneity:
Q(df = 233) = 81.5497, p-val = 1.0000
Model Results:
estimate se tval df pval ci.lb ci.ub
0.0207 0.0159 1.3021 60 0.1978 -0.0111 0.0524
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Code
# meta-regressionmod_chronic_reg <-rma.mv(yi = yi, V = VCV, random =list(~1|Species,~1|Phylogeny,~1|Effect_ID2), struct ="DIAG",R =list(Phylogeny = cor_tree), data = dat_long_chronic,method="REML", control=list(optimizer="optim", optmethod="Nelder-Mead"),mods =~type -1,test ="t",sparse=TRUE)summary(mod_chronic_reg)
Multivariate Meta-Analysis Model (k = 234; method: REML)
logLik Deviance AIC BIC AICc
113.9835 -227.9670 -215.9670 -195.3125 -215.5920
Variance Components:
estim sqrt nlvls fixed factor R
sigma^2.1 0.0000 0.0000 61 no Species no
sigma^2.2 0.0000 0.0000 61 no Phylogeny yes
sigma^2.3 0.0000 0.0000 234 no Effect_ID2 no
Test for Residual Heterogeneity:
QE(df = 231) = 78.4918, p-val = 1.0000
Test of Moderators (coefficients 1:3):
F(df1 = 3, df2 = 231) = 1.5845, p-val = 0.1939
Model Results:
estimate se tval df pval ci.lb ci.ub
typeUpper 0.0233 0.0267 0.8734 231 0.3834 -0.0293 0.0759
typeMedian 0.0034 0.0187 0.1791 231 0.8580 -0.0335 0.0402
typeLower 0.0364 0.0183 1.9874 231 0.0481 0.0003 0.0725 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Code
p_chronic <-orchard_plot(mod_chronic_reg, mod ="type",xlab ="log risk difference (Chronic Disease)", group ="Species") +ylim(-0.85, 0.7)p_chronic
Code
# sex_type dat_long_chronic$sex_type <-as.factor(paste0(dat_long_chronic$Sex, "_", dat_long_chronic$type))mod_chronic_reg2 <-rma.mv(yi = yi, V = VCV, random =list(~1|Species,~1|Phylogeny,~1|Effect_ID2), #struct = "DIAG",R =list(Phylogeny = cor_tree), data = dat_long_chronic,method="REML", control=list(optimizer="optim", optmethod="Nelder-Mead"),mods =~ sex_type -1,sparse=TRUE)summary(mod_chronic_reg2)
Multivariate Meta-Analysis Model (k = 234; method: REML)
logLik Deviance AIC BIC AICc
111.4340 -222.8680 -204.8680 -174.0039 -204.0423
Variance Components:
estim sqrt nlvls fixed factor R
sigma^2.1 0.0000 0.0000 61 no Species no
sigma^2.2 0.0000 0.0000 61 no Phylogeny yes
sigma^2.3 0.0000 0.0000 234 no Effect_ID2 no
Test for Residual Heterogeneity:
QE(df = 228) = 78.3678, p-val = 1.0000
Test of Moderators (coefficients 1:6):
QM(df = 6) = 4.8775, p-val = 0.5596
Model Results:
estimate se zval pval ci.lb ci.ub
sex_typeFemale_Lower 0.0332 0.0284 1.1711 0.2415 -0.0224 0.0888
sex_typeFemale_Median -0.0047 0.0301 -0.1546 0.8771 -0.0636 0.0543
sex_typeFemale_Upper 0.0206 0.0434 0.4741 0.6354 -0.0644 0.1055
sex_typeMale_Lower 0.0386 0.0240 1.6042 0.1087 -0.0085 0.0857
sex_typeMale_Median 0.0084 0.0239 0.3525 0.7245 -0.0384 0.0552
sex_typeMale_Upper 0.0250 0.0339 0.7377 0.4607 -0.0414 0.0913
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Code
p_chronic2 <-orchard_plot(mod_chronic_reg2, mod ="sex_type",xlab ="log risk difference \n(Chronic Disease)", group ="Species", flip = F) +ylim(-0.85, 0.7)p_chronic2
Code
# mod_tableres_chronic <-mod_results(mod_chronic_reg2, mod ="sex_type", group ="Species")attr(res_chronic, "class") <-NULLres_chronic$mod_table$name <-paste(res_chronic$mod_table$name, "Chronic", sep ="_")res_chronic$mod_table$name <-factor(res_chronic$mod_table$name)res_chronic$data$moderator <-paste(res_chronic$data$moderator, "Chronic", sep ="_")res_chronic$data$moderator <-factor(res_chronic$data$moderator)
Code
#################### Death at birth#################### Lowdat_deathAtBirth <-escalc(measure ="RD", ai = Contra_deathAtBirth_Low*Contra_deathAtBirth_N,bi = (1-Contra_deathAtBirth_Low)*Contra_deathAtBirth_N,ci = noContra_deathAtBirth_Low*noContra_deathAtBirth_N,di = (1-noContra_deathAtBirth_Low)*noContra_deathAtBirth_N,var.names =c("yi_deathAtBirth_low", "vi_deathAtBirth_low"),data = dat)# Meddat_deathAtBirth <-escalc(measure ="RD",ai = Contra_deathAtBirth_Med*Contra_deathAtBirth_N, bi = (1-Contra_deathAtBirth_Med)*Contra_deathAtBirth_N, ci = noContra_deathAtBirth_Med*noContra_deathAtBirth_N,di = (1-noContra_deathAtBirth_Med)*noContra_deathAtBirth_N,var.names =c("yi_deathAtBirth_med", "vi_deathAtBirth_med"),data = dat_deathAtBirth)# Uppdat_deathAtBirth <-escalc(measure ="RD",ai = Contra_deathAtBirth_Upp*Contra_deathAtBirth_N, bi = (1-Contra_deathAtBirth_Upp)*Contra_deathAtBirth_N, ci = noContra_deathAtBirth_Upp*noContra_deathAtBirth_N,di = (1-noContra_deathAtBirth_Upp)*noContra_deathAtBirth_N,var.names =c("yi_deathAtBirth_upp", "vi_deathAtBirth_upp"),data = dat_deathAtBirth)dat_deathAtBirth %>%filter(Contra_deathAtBirth_N >0) -> dat_deathAtBirth# create a long format of the data using these 3 types of effect sizes (low, med, upp) yi and vi are the effect size and variance of the effect sizedat_long_deathAtBirth <- dat_deathAtBirth %>%select(Effect_ID, Species, Phylogeny, Sex_Type,Sex, yi_deathAtBirth_low, vi_deathAtBirth_low, yi_deathAtBirth_med, vi_deathAtBirth_med, yi_deathAtBirth_upp, vi_deathAtBirth_upp) %>%pivot_longer(cols =c(yi_deathAtBirth_low, yi_deathAtBirth_med, yi_deathAtBirth_upp, vi_deathAtBirth_low, vi_deathAtBirth_med, vi_deathAtBirth_upp), names_to =c(".value", "type"), names_pattern ="(yi|vi)_(.*)")dat_long_deathAtBirth <- dat_long_deathAtBirth %>%filter(!is.na(yi))str(dat_long_deathAtBirth)
dat_long_deathAtBirth$type <-factor(dat_long_deathAtBirth$type, levels =rev(c("deathAtBirth_low", "deathAtBirth_med", "deathAtBirth_upp")),labels =rev(c("Lower", "Median", "Upper")) )# effect size level IDdat_long_deathAtBirth$Effect_ID2 <-factor(1:nrow(dat_long_deathAtBirth))# VCVVCV <-vcalc(dat_long_deathAtBirth$vi, cluster = dat_long_deathAtBirth$Effect_ID, obs = dat_long_deathAtBirth$Effect_ID2, data = dat_long_deathAtBirth, rho =0.5)# meta-analysis using dat_longmod_deathAtBirth <-rma.mv(yi = yi, V = VCV, random =list(~1|Species,~1|Phylogeny,~1|Effect_ID2), R =list(Phylogeny = cor_tree), data = dat_long_deathAtBirth,method="REML", test ="t",control=list(optimizer="optim", optmethod="Nelder-Mead"))summary(mod_deathAtBirth)
Multivariate Meta-Analysis Model (k = 24; method: REML)
logLik Deviance AIC BIC AICc
2.3380 -4.6760 3.3240 7.8660 5.5462
Variance Components:
estim sqrt nlvls fixed factor R
sigma^2.1 0.0000 0.0000 8 no Species no
sigma^2.2 0.0000 0.0000 8 no Phylogeny yes
sigma^2.3 0.0000 0.0000 24 no Effect_ID2 no
Test for Heterogeneity:
Q(df = 23) = 3.0601, p-val = 1.0000
Model Results:
estimate se tval df pval ci.lb ci.ub
-0.0187 0.0965 -0.1934 23 0.8483 -0.2183 0.1809
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Code
# meta-regressionmod_deathAtBirth_reg <-rma.mv(yi = yi, V = VCV, random =list(~1|Species,~1|Phylogeny,~1|Effect_ID2), #struct = "DIAG",R =list(Phylogeny = cor_tree), data = dat_long_deathAtBirth,method="REML", control=list(optimizer="optim", optmethod="Nelder-Mead"),mods =~type -1,test ="t",sparse=FALSE)summary(mod_deathAtBirth_reg)
Multivariate Meta-Analysis Model (k = 24; method: REML)
logLik Deviance AIC BIC AICc
2.0187 -4.0374 7.9626 14.2298 13.9626
Variance Components:
estim sqrt nlvls fixed factor R
sigma^2.1 0.0000 0.0000 8 no Species no
sigma^2.2 0.0000 0.0000 8 no Phylogeny yes
sigma^2.3 0.0000 0.0000 24 no Effect_ID2 no
Test for Residual Heterogeneity:
QE(df = 21) = 1.6796, p-val = 1.0000
Test of Moderators (coefficients 1:3):
F(df1 = 3, df2 = 21) = 0.4726, p-val = 0.7046
Model Results:
estimate se tval df pval ci.lb ci.ub
typeUpper -0.0799 0.1192 -0.6703 21 0.5100 -0.3278 0.1680
typeMedian -0.0350 0.1182 -0.2962 21 0.7700 -0.2809 0.2109
typeLower 0.0594 0.1193 0.4977 21 0.6239 -0.1888 0.3076
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Code
p_deathAtBirth <-orchard_plot(mod_deathAtBirth_reg, mod ="type",xlab ="log risk difference (Death at Birth)", group ="Species") +ylim(-0.85, 0.7)p_deathAtBirth
Code
# sex_type dat_long_deathAtBirth$sex_type <-as.factor(paste0(dat_long_deathAtBirth$Sex, "_", dat_long_deathAtBirth$type))mod_deathAtBirth_reg2 <-rma.mv(yi = yi, V = VCV, random =list(~1|Species,~1|Phylogeny,~1|Effect_ID2), #struct = "DIAG",R =list(Phylogeny = cor_tree), data = dat_long_deathAtBirth,method="REML", control=list(optimizer="optim", optmethod="Nelder-Mead"),mods =~ sex_type -1,sparse=TRUE)summary(mod_deathAtBirth_reg2)
Multivariate Meta-Analysis Model (k = 24; method: REML)
logLik Deviance AIC BIC AICc
2.0187 -4.0374 7.9626 14.2298 13.9626
Variance Components:
estim sqrt nlvls fixed factor R
sigma^2.1 0.0000 0.0000 8 no Species no
sigma^2.2 0.0000 0.0000 8 no Phylogeny yes
sigma^2.3 0.0000 0.0000 24 no Effect_ID2 no
Test for Residual Heterogeneity:
QE(df = 21) = 1.6796, p-val = 1.0000
Test of Moderators (coefficients 1:3):
QM(df = 3) = 1.4179, p-val = 0.7013
Model Results:
estimate se zval pval ci.lb ci.ub
sex_typeFemale_Lower 0.0594 0.1193 0.4977 0.6187 -0.1745 0.2933
sex_typeFemale_Median -0.0350 0.1182 -0.2962 0.7671 -0.2667 0.1967
sex_typeFemale_Upper -0.0799 0.1192 -0.6703 0.5027 -0.3135 0.1537
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Code
# mod_tableres_deathAtBirth <-mod_results(mod_deathAtBirth_reg2, mod ="sex_type", group ="Species")attr(res_deathAtBirth, "class") <-NULLres_deathAtBirth$mod_table$name <-paste(res_deathAtBirth$mod_table$name, "Death", sep ="_")res_deathAtBirth$mod_table$name <-factor(res_deathAtBirth$mod_table$name)res_deathAtBirth$data$moderator <-paste(res_deathAtBirth$data$moderator, "Death", sep ="_")res_deathAtBirth$data$moderator <-factor(res_deathAtBirth$data$moderator)p_deathAtBirth2 <-orchard_plot(mod_deathAtBirth_reg2, mod ="sex_type",xlab ="log risk difference \n(Death at birth)", group ="Species", flip = F) +ylim(-0.85, 0.7)p_deathAtBirth2
Code
######### Other######### Lowdat_other <-escalc(measure ="RD", ai = Contra_Other_Low*Contra_Other_N,bi = (1-Contra_Other_Low)*Contra_Other_N,ci = noContra_Other_Low*noContra_Other_N,di = (1-noContra_Other_Low)*noContra_Other_N,var.names =c("yi_other_low", "vi_other_low"),data = dat)# Meddat_other <-escalc(measure ="RD",ai = Contra_Other_Med*Contra_Other_N, bi = (1-Contra_Other_Med)*Contra_Other_N, ci = noContra_Other_Med*noContra_Other_N,di = (1-noContra_Other_Med)*noContra_Other_N,var.names =c("yi_other_med", "vi_other_med"),data = dat_other)# Uppdat_other <-escalc(measure ="RD",ai = Contra_Other_Upp*Contra_Other_N, bi = (1-Contra_Other_Upp)*Contra_Other_N, ci = noContra_Other_Upp*noContra_Other_N,di = (1-noContra_Other_Upp)*noContra_Other_N,var.names =c("yi_other_upp", "vi_other_upp"),data = dat_other)dat_other %>%filter(Contra_Other_N >0) -> dat_other# create a long format of the data using these 3 types of effect sizes (low, med, upp) yi and vi are the effect size and variance of the effect sizedat_long_other <- dat_other %>%select(Effect_ID, Species, Phylogeny, Sex_Type,Sex, yi_other_low, vi_other_low, yi_other_med, vi_other_med, yi_other_upp, vi_other_upp) %>%pivot_longer(cols =c(yi_other_low, yi_other_med, yi_other_upp, vi_other_low, vi_other_med, vi_other_upp), names_to =c(".value", "type"), names_pattern ="(yi|vi)_(.*)")dat_long_other <- dat_long_other %>%filter(!is.na(yi))str(dat_long_other)